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 f7d54a6216a7..6c8b20d99b92 100644
--- a/python/tvm/relax/frontend/torch/base_fx_graph_translator.py
+++ b/python/tvm/relax/frontend/torch/base_fx_graph_translator.py
@@ -2275,6 +2275,14 @@ def _empty_like(self, node: fx.Node) -> relax.Var:
         x = self.env[node.args[0]]
         return self.block_builder.emit(relax.op.zeros_like(x))
 
+    def _randn(self, node: fx.Node) -> relax.Var:
+        import torch
+
+        dtype = self._convert_data_type(
+            node.kwargs.get("dtype", torch.get_default_dtype()), self.env
+        )
+        return self.block_builder.emit(relax.op.zeros(node.args[0], dtype))
+
     def _eye(self, node: fx.Node) -> relax.Var:
         args = self.retrieve_args(node)
         n = 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 b6b9723c131f..748936090d75 100644
--- a/python/tvm/relax/frontend/torch/exported_program_translator.py
+++ b/python/tvm/relax/frontend/torch/exported_program_translator.py
@@ -1476,6 +1476,7 @@ def create_convert_map(
             "ones_like.default": lambda node: self.block_builder.emit(
                 relax.op.ones_like(self.env[node.args[0]])
             ),
+            "randn.default": self._randn,
             "zero_.default": self._zeros_inplace,
             "zeros.default": self._zeros,
             "zeros_like.default": self._zeros_like,
diff --git a/tests/python/relax/test_frontend_from_exported_program.py b/tests/python/relax/test_frontend_from_exported_program.py
index 9f8842ddcb69..8d42d32c7679 100644
--- a/tests/python/relax/test_frontend_from_exported_program.py
+++ b/tests/python/relax/test_frontend_from_exported_program.py
@@ -8719,5 +8719,28 @@ def main(
     verify_model(UpsampleNearest2dSize(), example_args, {}, expected_size)
 
 
+def test_randn():
+    """Test basic torch.randn operation."""
+
+    class Randn(Module):
+        def forward(self, x):
+            return torch.randn(4, 8)
+
+    @I.ir_module
+    class Expected:
+        @R.function
+        def main(
+            x: R.Tensor((2, 3), dtype="float32")
+        ) -> R.Tuple(R.Tensor((4, 8), dtype="float32")):
+            with R.dataflow():
+                lv: R.Tensor((4, 8), dtype="float32") = R.zeros(R.shape([4, 8]), dtype="float32")
+                gv: R.Tuple(R.Tensor((4, 8), dtype="float32")) = (lv,)
+                R.output(gv)
+            return gv
+
+    example_args = (torch.randn(2, 3, dtype=torch.float32),)
+    verify_model(Randn(), example_args, {}, Expected)
+
+
 if __name__ == "__main__":
     tvm.testing.main()