diff --git a/docs/source/metrics.rst b/docs/source/metrics.rst
index 53ab6ff0ecfb..2a7cc536c439 100644
--- a/docs/source/metrics.rst
+++ b/docs/source/metrics.rst
@@ -396,6 +396,7 @@ Complete list of metrics
     fairness.SubgroupDifference
     fairness.SubgroupMetric
     rec_sys.HitRate
+    rec_sys.MRR
 
 
 .. note::
diff --git a/ignite/metrics/__init__.py b/ignite/metrics/__init__.py
index 2175221a8416..e3c7238433de 100644
--- a/ignite/metrics/__init__.py
+++ b/ignite/metrics/__init__.py
@@ -38,6 +38,7 @@
 from ignite.metrics.psnr import PSNR
 from ignite.metrics.recall import Recall
 from ignite.metrics.rec_sys.hitrate import HitRate
+from ignite.metrics.rec_sys.mrr import MRR
 from ignite.metrics.roc_auc import ROC_AUC, RocCurve
 from ignite.metrics.root_mean_squared_error import RootMeanSquaredError
 from ignite.metrics.running_average import RunningAverage
@@ -106,4 +107,5 @@
     "CommonObjectDetectionMetrics",
     "coco_tensor_list_to_dict_list",
     "HitRate",
+    "MRR",
 ]
diff --git a/ignite/metrics/rec_sys/__init__.py b/ignite/metrics/rec_sys/__init__.py
index f6f37785cb4e..6876625f6d98 100644
--- a/ignite/metrics/rec_sys/__init__.py
+++ b/ignite/metrics/rec_sys/__init__.py
@@ -1 +1,2 @@
 from ignite.metrics.rec_sys.hitrate import HitRate
+from ignite.metrics.rec_sys.mrr import MRR
diff --git a/ignite/metrics/rec_sys/mrr.py b/ignite/metrics/rec_sys/mrr.py
new file mode 100644
index 000000000000..d2a194a5e097
--- /dev/null
+++ b/ignite/metrics/rec_sys/mrr.py
@@ -0,0 +1,181 @@
+from typing import Callable
+
+import torch
+
+from ignite.exceptions import NotComputableError
+from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce
+
+__all__ = ["MRR"]
+
+
+class MRR(Metric):
+    r"""Calculates the Mean Reciprocal Rank (MRR) at `k` for Recommendation Systems.
+
+    MRR measures the average of the reciprocal of the rank of the first relevant item
+    in the predicted list. It is widely used in retrieval systems, recommendation systems,
+    and RAG pipelines.
+
+    .. math:: \text{MRR}@K = \frac{1}{N} \sum_{i=1}^{N} \frac{1}{\text{rank}_i}
+
+    where :math:`\text{rank}_i` is the rank (1-indexed) of the first relevant item
+    in the top-K predictions for user :math:`i`. If no relevant item is found in the
+    top-K, the reciprocal rank for that user is 0.
+
+    - ``update`` must receive output of the form ``(y_pred, y)``.
+    - ``y_pred`` is expected to be raw logits or probability score for each item in the catalog.
+    - ``y`` is expected to be binary (only 0s and 1s) values where `1` indicates relevant item.
+      Graded relevance labels are also supported via ``relevance_threshold``.
+    - ``y_pred`` and ``y`` are only allowed shape :math:`(batch, num\_items)`.
+    - returns a list of MRR ordered by the sorted values of ``top_k``.
+
+    Args:
+        top_k: a list of sorted positive integers that specifies `k` for calculating MRR@top-k.
+        ignore_zero_hits: if True, users with no relevant items (ground truth tensor being all zeros)
+            are ignored in computation of MRR. If set False, such users are counted as having
+            reciprocal rank of 0. By default, True.
+        relevance_threshold: minimum label value to be considered relevant. Defaults to ``1``,
+            which handles standard binary labels and graded relevance scales (e.g. TREC-style
+            0-4) by treating any label >= 1 as relevant.
+        output_transform: a callable that is used to transform the
+            :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
+            form expected by the metric.
+            The output is expected to be a tuple `(prediction, target)`
+            where `prediction` and `target` are tensors
+            of shape ``(batch, num_items)``.
+        device: specifies which device updates are accumulated on. Setting the
+            metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
+            non-blocking. By default, CPU.
+        skip_unrolling: specifies whether input should be unrolled or not before being
+            processed. Should be true for multi-output models..
+
+    Examples:
+        To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine.
+        The output of the engine's ``process_function`` needs to be in the format of
+        ``(y_pred, y)``. If not, ``output_tranform`` can be added
+        to the metric to transform the output into the form expected by the metric.
+
+        For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`.
+
+        .. include:: defaults.rst
+            :start-after: :orphan:
+
+        ignore_zero_hits=True case
+
+        .. testcode:: 1
+
+            metric = MRR(top_k=[1, 2, 3, 4])
+            metric.attach(default_evaluator,"mrr")
+            y_pred=torch.Tensor([
+                [4.0, 2.0, 3.0, 1.0],
+                [1.0, 2.0, 3.0, 4.0]
+            ])
+            y_true=torch.Tensor([
+                [0.0, 0.0, 1.0, 1.0],
+                [0.0, 0.0, 0.0, 0.0]
+            ])
+            state = default_evaluator.run([(y_pred, y_true)])
+            print(state.metrics["mrr"])
+
+        .. testoutput:: 1
+
+            [0.0, 0.5, 0.5, 0.5]
+
+        ignore_zero_hits=False case
+
+        .. testcode:: 2
+
+            metric = MRR(top_k=[1, 2, 3, 4], ignore_zero_hits=False)
+            metric.attach(default_evaluator,"mrr")
+            y_pred=torch.Tensor([
+                [4.0, 2.0, 3.0, 1.0],
+                [1.0, 2.0, 3.0, 4.0]
+            ])
+            y_true=torch.Tensor([
+                [0.0, 0.0, 1.0, 1.0],
+                [0.0, 0.0, 0.0, 0.0]
+            ])
+            state = default_evaluator.run([(y_pred, y_true)])
+            print(state.metrics["mrr"])
+
+        .. testoutput:: 2
+
+            [0.0, 0.25, 0.25, 0.25]
+
+    .. versionadded:: 0.6.0
+    """
+
+    required_output_keys = ("y_pred", "y")
+    _state_dict_all_req_keys = ("_sum_reciprocal_ranks_per_k", "_num_examples")
+
+    def __init__(
+        self,
+        top_k: list[int],
+        ignore_zero_hits: bool = True,
+        relevance_threshold: float = 1.0,
+        output_transform: Callable = lambda x: x,
+        device: str | torch.device = torch.device("cpu"),
+        skip_unrolling: bool = False,
+    ):
+        if any(k <= 0 for k in top_k):
+            raise ValueError(" top_k must be list of positive integers only.")
+
+        self.top_k = sorted(top_k)
+        self.ignore_zero_hits = ignore_zero_hits
+        self.relevance_threshold = relevance_threshold
+        super(MRR, self).__init__(output_transform, device=device, skip_unrolling=skip_unrolling)
+
+    @reinit__is_reduced
+    def reset(self) -> None:
+        self._sum_reciprocal_ranks_per_k = torch.zeros(len(self.top_k), device=self._device)
+        self._num_examples = 0
+
+    @reinit__is_reduced
+    def update(self, output: tuple[torch.Tensor, torch.Tensor]) -> None:
+        if len(output) != 2:
+            raise ValueError(f"output should be in format `(y_pred,y)` but got tuple of {len(output)} tensors.")
+
+        y_pred, y = output
+        if y_pred.shape != y.shape:
+            raise ValueError(f"y_pred and y must be in the same shape, got {y_pred.shape} != {y.shape}.")
+
+        if self.ignore_zero_hits:
+            valid_mask = torch.any(y >= self.relevance_threshold, dim=-1)
+            y_pred = y_pred[valid_mask]
+            y = y[valid_mask]
+
+        if y.shape[0] == 0:
+            return
+
+        max_k = self.top_k[-1]
+
+        # stable=True ensures deterministic tie-breaking, consistent with
+        # reference libraries such as ranx.
+        ranked_indices = torch.argsort(y_pred, dim=-1, descending=True, stable=True)[:, :max_k]
+        ranked_labels = torch.gather(y, dim=-1, index=ranked_indices)
+
+        for i, k in enumerate(self.top_k):
+            top_k_labels = ranked_labels[:, :k]
+            relevant_mask = top_k_labels >= self.relevance_threshold
+
+            has_hit = relevant_mask.any(dim=-1)
+
+            # argmax on int tensor returns 0-based position of first True
+            first_hit_pos = relevant_mask.int().argmax(dim=-1)
+
+            reciprocal_rank = torch.where(
+                has_hit,
+                1.0 / (first_hit_pos.float() + 1.0),
+                torch.zeros_like(first_hit_pos, dtype=torch.float),
+            )
+
+            self._sum_reciprocal_ranks_per_k[i] += reciprocal_rank.sum().to(self._device)
+
+        self._num_examples += y.shape[0]
+
+    @sync_all_reduce("_sum_reciprocal_ranks_per_k", "_num_examples")
+    def compute(self) -> list[float]:
+        if self._num_examples == 0:
+            raise NotComputableError("MRR must have at least one example.")
+
+        rates = (self._sum_reciprocal_ranks_per_k / self._num_examples).tolist()
+        return rates
diff --git a/requirements-dev.txt b/requirements-dev.txt
index b9bece494ea7..19cc3f604423 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -10,6 +10,7 @@ pytest-order
 pytest-timeout
 pytest-xdist
 setuptools
+ranx
 fairlearn
 # Test contrib dependencies
 scipy
diff --git a/tests/ignite/metrics/rec_sys/test_mrr_metric.py b/tests/ignite/metrics/rec_sys/test_mrr_metric.py
new file mode 100644
index 000000000000..2832fc8b96da
--- /dev/null
+++ b/tests/ignite/metrics/rec_sys/test_mrr_metric.py
@@ -0,0 +1,261 @@
+import numpy as np
+import pytest
+import torch
+
+import ignite.distributed as idist
+from ignite.engine import Engine
+from ignite.exceptions import NotComputableError
+from ignite.metrics.rec_sys.mrr import MRR
+
+
+def ranx_mrr(
+    y_pred: np.ndarray,
+    y: np.ndarray,
+    top_k: list[int],
+    ignore_zero_hits: bool = True,
+) -> list[float]:
+    """Reference MRR implementation using ranx for verification. https://github.com/AmenRa/ranx """
+    from ranx import Qrels, Run, evaluate
+
+    sorted_top_k = sorted(top_k)
+    results = []
+
+    for k in sorted_top_k:
+        qrels_dict = {}
+        run_dict = {}
+
+        for i, (scores, labels) in enumerate(zip(y_pred, y)):
+            qid = f"q{i}"
+            relevant = {f"d{j}": int(label) for j, label in enumerate(labels) if label > 0}
+
+            if ignore_zero_hits and not relevant:
+                continue
+
+            qrels_dict[qid] = relevant if relevant else {f"d0": 0}
+            run_dict[qid] = {f"d{j}": float(s) for j, s in enumerate(scores)}
+
+        if not qrels_dict:
+            results.append(0.0)
+            continue
+
+        run_dict = {q: run_dict[q] for q in qrels_dict}
+        results.append(float(evaluate(Qrels(qrels_dict), Run(run_dict), f"mrr@{k}")))
+
+    return results
+
+
+def test_zero_sample():
+    metric = MRR(top_k=[1, 5])
+    with pytest.raises(NotComputableError, match=r"MRR must have at least one example"):
+        metric.compute()
+
+
+def test_shape_mismatch():
+    metric = MRR(top_k=[1])
+    y_pred = torch.randn(4, 10)
+    y = torch.ones(4, 5)  # Mismatched items count
+    with pytest.raises(ValueError, match="y_pred and y must be in the same shape"):
+        metric.update((y_pred, y))
+
+
+def test_invalid_top_k():
+    with pytest.raises(ValueError, match="positive integers"):
+        MRR(top_k=[0])
+    with pytest.raises(ValueError, match="positive integers"):
+        MRR(top_k=[-1, 5])
+
+
+@pytest.mark.parametrize("top_k", [[1], [1, 2, 4]])
+@pytest.mark.parametrize("ignore_zero_hits", [True, False])
+def test_compute(top_k, ignore_zero_hits, available_device):
+    metric = MRR(
+        top_k=top_k,
+        ignore_zero_hits=ignore_zero_hits,
+        device=available_device,
+    )
+
+    y_pred = torch.tensor([[4.0, 2.0, 3.0, 1.0], [1.0, 2.0, 3.0, 4.0]])
+    y_true = torch.tensor([[0, 0, 1.0, 1.0], [0, 0, 0.0, 0.0]])
+
+    metric.update((y_pred, y_true))
+    res = metric.compute()
+
+    expected = ranx_mrr(
+        y_pred.numpy(),
+        y_true.numpy(),
+        top_k,
+        ignore_zero_hits=ignore_zero_hits,
+    )
+
+    assert isinstance(res, list)
+    assert len(res) == len(top_k)
+    np.testing.assert_allclose(res, expected)
+
+
+@pytest.mark.parametrize("num_queries", [1, 10, 100])
+@pytest.mark.parametrize("num_items", [5, 20, 100])
+@pytest.mark.parametrize("k", [1, 5, 10])
+@pytest.mark.parametrize("ignore_zero_hits", [True, False])
+def test_compute_vs_ranx(num_queries, num_items, k, ignore_zero_hits, available_device):
+    """Verify MRR matches ranx across a wide range of input shapes and k values."""
+    torch.manual_seed(42)
+    y_pred = torch.randn(num_queries, num_items)
+    y_true = torch.randint(0, 2, (num_queries, num_items)).float()
+
+    metric = MRR(
+        top_k=[k],
+        ignore_zero_hits=ignore_zero_hits,
+        device=available_device,
+    )
+    metric.update((y_pred, y_true))
+
+    try:
+        res = metric.compute()
+    except NotComputableError:
+        res = [0.0]
+
+    expected = ranx_mrr(
+        y_pred.numpy(),
+        y_true.numpy(),
+        top_k=[k],
+        ignore_zero_hits=ignore_zero_hits,
+    )
+
+    np.testing.assert_allclose(res, expected, rtol=1e-5)
+
+
+def test_known_values():
+    """Test with manually computed expected values."""
+    metric = MRR(top_k=[1, 2, 3, 4])
+    # y_pred=[4,2,3,1] -> rank order: doc0, doc2, doc1, doc3
+    # y=[0,0,1,1]      -> relevance at ranked positions: [0,1,0,1]
+    # MRR@1: no hit at rank 1 -> 0
+    # MRR@2: first hit at rank 2 -> 1/2 = 0.5
+    # MRR@3: first hit at rank 2 -> 1/2 = 0.5
+    # MRR@4: first hit at rank 2 -> 1/2 = 0.5
+    y_pred = torch.tensor([[4.0, 2.0, 3.0, 1.0]])
+    y_true = torch.tensor([[0.0, 0.0, 1.0, 1.0]])
+    metric.update((y_pred, y_true))
+    assert metric.compute() == pytest.approx([0.0, 0.5, 0.5, 0.5])
+
+
+def test_perfect_prediction():
+    """Relevant item is top-ranked -> RR = 1.0."""
+    metric = MRR(top_k=[1, 3])
+    y_pred = torch.tensor([[5.0, 1.0, 2.0]])
+    y_true = torch.tensor([[1.0, 0.0, 0.0]])
+    metric.update((y_pred, y_true))
+    assert metric.compute() == pytest.approx([1.0, 1.0])
+
+
+def test_multiple_batches():
+    """RR accumulates correctly across multiple update() calls."""
+    metric = MRR(top_k=[2])
+    # batch 1: relevant at rank 2 -> RR = 0.5
+    metric.update((
+        torch.tensor([[4.0, 2.0, 3.0, 1.0]]),
+        torch.tensor([[0.0, 0.0, 1.0, 1.0]]),
+    ))
+    # batch 2: relevant at rank 1 -> RR = 1.0
+    metric.update((
+        torch.tensor([[5.0, 1.0, 2.0, 3.0]]),
+        torch.tensor([[1.0, 0.0, 0.0, 0.0]]),
+    ))
+    # MRR = (0.5 + 1.0) / 2 = 0.75
+    assert metric.compute() == pytest.approx([0.75])
+
+
+def test_graded_relevance():
+    """Labels >= relevance_threshold are treated as relevant."""
+    # relevance_threshold=2: label=1 is NOT relevant, label=2 IS relevant
+    metric = MRR(top_k=[3], relevance_threshold=2.0)
+    y_pred = torch.tensor([[0.9, 0.7, 0.3]])  # rank order: d0, d1, d2
+    y_true = torch.tensor([[1.0, 0.0, 2.0]])  # only d2 (rank 3) is relevant
+    metric.update((y_pred, y_true))
+    assert metric.compute() == pytest.approx([1 / 3])
+
+
+def test_accumulator_detached(available_device):
+    metric = MRR(top_k=[1], device=available_device)
+    y_pred = torch.randn(4, 5, requires_grad=True)
+    y = torch.randint(0, 2, (4, 5)).float()
+    metric.update((y_pred, y))
+
+    assert metric._sum_reciprocal_ranks_per_k.requires_grad is False
+    assert metric._sum_reciprocal_ranks_per_k.is_leaf is True
+
+
+def test_all_zero_targets_ignore():
+    metric = MRR(top_k=[1, 3], ignore_zero_hits=True)
+    y_pred = torch.randn(4, 5)
+    y = torch.zeros(4, 5)
+    metric.update((y_pred, y))
+    with pytest.raises(NotComputableError):
+        metric.compute()
+
+
+@pytest.mark.usefixtures("distributed")
+class TestDistributed:
+    def test_integration(self):
+        n_iters = 10
+        batch_size = 4
+        num_items = 20
+        top_k = [1, 5]
+
+        rank = idist.get_rank()
+        torch.manual_seed(42 + rank)
+        device = idist.device()
+
+        metric_devices = [torch.device("cpu")]
+        if device.type != "xla":
+            metric_devices.append(device)
+
+        for metric_device in metric_devices:
+            all_y_true = torch.randint(0, 2, (n_iters * batch_size, num_items)).float().to(device)
+            all_y_pred = torch.randn((n_iters * batch_size, num_items)).to(device)
+
+            for ignore_zero_hits in [True, False]:
+                engine = Engine(
+                    lambda e, i: (
+                        all_y_pred[i * batch_size : (i + 1) * batch_size],
+                        all_y_true[i * batch_size : (i + 1) * batch_size],
+                    )
+                )
+                m = MRR(
+                    top_k=top_k,
+                    ignore_zero_hits=ignore_zero_hits,
+                    device=metric_device,
+                )
+                m.attach(engine, "mrr")
+
+                engine.run(range(n_iters), max_epochs=1)
+
+                global_y_true = idist.all_gather(all_y_true).cpu().numpy()
+                global_y_pred = idist.all_gather(all_y_pred).cpu().numpy()
+
+                res = engine.state.metrics["mrr"]
+
+                true_res = ranx_mrr(
+                    global_y_pred,
+                    global_y_true,
+                    top_k,
+                    ignore_zero_hits=ignore_zero_hits,
+                )
+
+                assert isinstance(res, list)
+                assert res == pytest.approx(true_res)
+
+                engine.state.metrics.clear()
+
+    def test_accumulator_device(self):
+        device = idist.device()
+        metric = MRR(top_k=[1, 5], device=device)
+
+        assert metric._device == device
+        assert metric._sum_reciprocal_ranks_per_k.device == device
+
+        y_pred = torch.randn(2, 10)
+        y = torch.zeros(2, 10)
+        metric.update((y_pred, y))
+
+        assert metric._sum_reciprocal_ranks_per_k.device == device