diff --git a/tests/lowering/reduction/test_prod.py b/tests/lowering/reduction/test_prod.py
new file mode 100644
index 000000000..5b38597d5
--- /dev/null
+++ b/tests/lowering/reduction/test_prod.py
@@ -0,0 +1,58 @@
+import torch
+import torch_ttnn
+import pytest
+import ttnn
+
+
+class ProdDimIntModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, input, dim, keep_dim):
+        return torch.prod(input, dim, keep_dim)
+
+
+@pytest.mark.parametrize(
+    "input_shape, dim, keep_dim",
+    [
+        ((1, 2, 32, 32), 3, True),
+        ((1, 1, 32, 32), 2, True),
+        ((1, 2, 32, 32), 1, True),
+        ((2, 1, 32, 32), 0, True),
+        ((2, 1, 32, 32), 0, True),
+        pytest.param(
+            (1, 1, 2, 32, 32), -1, True, marks=pytest.mark.xfail(reason="Unexpected output shape [1, 1, 2, 1] (#244)")
+        ),
+        pytest.param((1, 2, 32, 32), 3, False, marks=pytest.mark.xfail(reason="Not support keep_dim = False (#244)")),
+        pytest.param(
+            (1, 1, 1, 32, 32),
+            4,
+            True,
+            marks=pytest.mark.xfail(
+                reason='dim >= -4 && dim <= 3 && "Dimension out of range (expected to be in range of [-4, 3]" (#244)'
+            ),
+        ),
+        pytest.param(
+            (1, 1, 32, 16), -1, True, marks=pytest.mark.xfail(reason="Need to pad with 1.0 instead of 0 (#244)")
+        ),
+        pytest.param((32, 32), 1, True, marks=pytest.mark.xfail(reason="Input rank can't < 4")),
+    ],
+)
+def test_prod_dim_int(device, input_shape, dim, keep_dim):
+    m = ProdDimIntModule()
+    input = torch.rand(input_shape, dtype=torch.bfloat16) + 0.5
+    result_before = m.forward(input, dim, keep_dim)
+
+    option = torch_ttnn.TorchTtnnOption(device=device, gen_graphviz=True)
+    # The compilation is lazy, so we need to run forward once to trigger the compilation
+    m = torch.compile(m, backend=torch_ttnn.backend, options=option)
+    result_after = m.forward(input, dim, keep_dim)
+    option._out_fx_graphs[0].print_tabular()
+
+    # Check the graph has be rewritten
+    nodes = list(option._out_fx_graphs[0].nodes)
+    assert [node.target for node in nodes].count(ttnn.prod) == 1
+    # Check inference result
+    assert result_before.shape == result_after.shape
+    # Give higher tolerance for product as it's not associative with float
+    assert torch.allclose(result_before, result_after, rtol=0.1)
diff --git a/torch_ttnn/passes/lowering/add_data_move_pass.py b/torch_ttnn/passes/lowering/add_data_move_pass.py
index 7add48c36..0f0974627 100644
--- a/torch_ttnn/passes/lowering/add_data_move_pass.py
+++ b/torch_ttnn/passes/lowering/add_data_move_pass.py
@@ -109,6 +109,11 @@ def is_function_call(node) -> bool:
     ttnn.where,
 ]
 
+TTNN_REDUCTION_OPS = [
+    ttnn.mean,
+    ttnn.prod,
+]
+
 TTNN_MATRIX_MULPIPLICATION_OPS = [
     ttnn.matmul,
     ttnn.linear,
@@ -147,6 +152,7 @@ def is_tt_compute(node) -> bool:
         TTNN_POINTWISE_UNARY_OPS
         + TTNN_POINTWISE_BINARY_OPS
         + TTNN_POINTWISE_TRINARY_OPS
+        + TTNN_REDUCTION_OPS
         + TTNN_MATRIX_MULPIPLICATION_OPS
         + TTNN_TARGET_WRAPPERS
         + TTNN_DATAMOVE_OPS
diff --git a/torch_ttnn/passes/lowering/to_tt_pass.py b/torch_ttnn/passes/lowering/to_tt_pass.py
index b0bb2c8f2..87876d410 100644
--- a/torch_ttnn/passes/lowering/to_tt_pass.py
+++ b/torch_ttnn/passes/lowering/to_tt_pass.py
@@ -623,6 +623,20 @@ def rewrite_node(node):
                     input = g.call_function(ttnn.to_layout, args=(input, TtnnRowMajorLayout()))
                 return g.call_function(ttnn.pad, args=(input, full_pad, value))
 
+            if node.target == torch.ops.aten.prod.dim_int:
+                input_shape = args[0].meta["val"].size()
+                if len(input_shape) != 4:
+                    return None
+                # TODO(#244): Not support keepdim = False (default value)
+                if len(args) < 3 or args[2] == False:
+                    return None
+                # TODO(#244): Not support non-tile-aligned shape
+                if len(input_shape) < 2 or any(size % ttnn.TILE_SIZE != 0 for size in input_shape[-2:]):
+                    return None
+                # Args: input, all_dimensions=false, dim
+                new_args = (args[0], False, args[1])
+                return g.call_function(ttnn.prod, new_args, kwargs)
+
         with g.inserting_before(node):
             new_node = rewrite_node(node)
             if new_node is not None: