diff --git a/python/tvm/relax/frontend/torch/base_fx_graph_translator.py b/python/tvm/relax/frontend/torch/base_fx_graph_translator.py
index 4c9480b58748..a83d2692bddb 100644
--- a/python/tvm/relax/frontend/torch/base_fx_graph_translator.py
+++ b/python/tvm/relax/frontend/torch/base_fx_graph_translator.py
@@ -1343,6 +1343,23 @@ def _index_select(self, node: fx.Node) -> relax.Var:
         index = self.env[node.args[2]]
         return self.block_builder.emit(relax.op.take(x, index, dim))
 
+    def _inplace_masked_fill(self, node: fx.Node) -> relax.Var:
+        x = self.env[node.args[0]]
+        mask = self.env[node.args[1]]
+        value = node.args[2]
+        rx_value = relax.const(value)
+        values = self.block_builder.emit(relax.op.full_like(x, rx_value))
+        output = self.block_builder.emit(relax.op.where(mask, values, x))
+        self.env[node.args[0]] = output
+        return output
+
+    def _masked_fill(self, node: fx.Node) -> relax.Var:
+        x = self.env[node.args[0]]
+        mask = self.env[node.args[1]]
+        rx_value = relax.const(node.args[2])
+        values = self.block_builder.emit(relax.op.full_like(x, rx_value))
+        return self.block_builder.emit(relax.op.where(mask, values, x))
+
     def _new_ones(self, node: fx.Node) -> relax.Var:
         args = self.retrieve_args(node)
         self_var = args[0]
diff --git a/python/tvm/relax/frontend/torch/exported_program_translator.py b/python/tvm/relax/frontend/torch/exported_program_translator.py
index c82a5e2b1100..57570d9810e4 100644
--- a/python/tvm/relax/frontend/torch/exported_program_translator.py
+++ b/python/tvm/relax/frontend/torch/exported_program_translator.py
@@ -447,6 +447,7 @@ def create_convert_map(
             "full_like.default": self._full_like,
             "index_select.default": self._index_select,
             "lift_fresh_copy.default": self._to_copy,
+            "masked_fill.Scalar": self._masked_fill,
             "new_ones.default": self._new_ones,
             "one_hot.default": self._one_hot,
             "ones.default": self._ones,
diff --git a/python/tvm/relax/frontend/torch/fx_translator.py b/python/tvm/relax/frontend/torch/fx_translator.py
index 297529e8bf29..50a3dc4a208d 100644
--- a/python/tvm/relax/frontend/torch/fx_translator.py
+++ b/python/tvm/relax/frontend/torch/fx_translator.py
@@ -468,23 +468,6 @@ def _inplace_fill(self, node: fx.Node) -> relax.Var:
         self.env[node.args[0]] = filled
         return filled
 
-    def _inplace_masked_fill(self, node: fx.Node) -> relax.Var:
-        x = self.env[node.args[0]]
-        mask = self.env[node.args[1]]
-        value = node.args[2]
-        rx_value = relax.const(value)
-        values = self.block_builder.emit(relax.op.full_like(x, rx_value))
-        output = self.block_builder.emit(relax.op.where(mask, values, x))
-        self.env[node.args[0]] = output
-        return output
-
-    def _masked_fill(self, node: fx.Node) -> relax.Var:
-        x = self.env[node.args[0]]
-        mask = self.env[node.args[1]]
-        rx_value = relax.const(node.args[2])
-        values = self.block_builder.emit(relax.op.full_like(x, rx_value))
-        return self.block_builder.emit(relax.op.where(mask, values, x))
-
     def _masked_scatter(self, node: fx.Node) -> relax.Var:
         x = self.env[node.args[0]]
         mask = self.env[node.args[1]]
diff --git a/tests/python/relax/test_frontend_from_exported_program.py b/tests/python/relax/test_frontend_from_exported_program.py
index 26d3d3f7bde2..43a7f7af5372 100644
--- a/tests/python/relax/test_frontend_from_exported_program.py
+++ b/tests/python/relax/test_frontend_from_exported_program.py
@@ -3224,6 +3224,30 @@ def main(
     verify_model(Fill(), example_args, {}, Expected)
 
 
+def test_masked_fill():
+    class Masked_Fill(Module):
+        def forward(self, input: torch.Tensor, mask: torch.Tensor):
+            return torch.masked_fill(input, mask, 0)
+
+    @tvm.script.ir_module
+    class Expected:
+        @R.function
+        def main(
+            input: R.Tensor((128, 128), dtype="float32"), mask: R.Tensor((128, 128), dtype="bool")
+        ) -> R.Tuple(R.Tensor((128, 128), dtype="float32")):
+            with R.dataflow():
+                lv: R.Tensor((128, 128), dtype="float32") = R.full_like(
+                    input, R.const(0, "int32"), dtype="void"
+                )
+                lv1: R.Tensor((128, 128), dtype="float32") = R.where(mask, lv, input)
+                gv: R.Tuple(R.Tensor((128, 128), dtype="float32")) = (lv1,)
+                R.output(gv)
+            return gv
+
+    example_args = (torch.randn(128, 128, dtype=torch.float32), torch.rand(128, 128) < 0.5)
+    verify_model(Masked_Fill(), example_args, {}, Expected)
+
+
 def test_new_ones():
     class NewOnes(Module):
         def forward(self, x):