diff --git a/python/tvm/relay/frontend/pytorch.py b/python/tvm/relay/frontend/pytorch.py
index 1b86b120dfcc..30f14b490b1b 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -2323,8 +2323,19 @@ def one_hot(self, inputs, input_types):
 
     def index(self, inputs, input_types):
         data = inputs[0]
-        indices = inputs[1]
-        return _op.adv_index([data] + indices)
+        indices_list = []
+
+        for indices in inputs[1]:
+            if self.infer_type(indices).dtype == "bool":
+                # adv_index does not support a mask as the index tensor (it will treat 0/1 as
+                # an index rather than a flag).
+                # So we use argwhere to turn the mask into indices, which will also take care
+                # of the dynamism in the indexing by mask.
+                indices_list.append(_op.squeeze(_op.transform.argwhere(indices), axis=[1]))
+            else:
+                indices_list.append(indices)
+
+        return _op.adv_index([data] + indices_list)
 
     def meshgrid(self, inputs, input_types):
         data = inputs[0]
diff --git a/tests/python/frontend/pytorch/test_forward.py b/tests/python/frontend/pytorch/test_forward.py
index 8045635127bb..36bb5bede475 100755
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -4034,6 +4034,14 @@ def forward(self, x):
     input_data = torch.rand(input_shape).float()
     verify_model(Index1().eval(), input_data=input_data)
 
+    def test_fn_bool_mask():
+        return lambda data, mask: data[0, mask]
+
+    data = torch.tensor([[1, 2, 3], [4, 5, 6]])
+    mask = torch.tensor([True, True, False])
+
+    verify_trace_model(test_fn_bool_mask(), [data, mask], ["llvm", "cuda"])
+
 
 def test_logsumexp():
     """test_logsumexp"""