diff --git a/ivy/functional/frontends/torch/tensor.py b/ivy/functional/frontends/torch/tensor.py
index 2db2826ac45ee..a364b4efc6204 100644
--- a/ivy/functional/frontends/torch/tensor.py
+++ b/ivy/functional/frontends/torch/tensor.py
@@ -266,6 +266,9 @@ def abs(self):
     def abs_(self):
         self._ivy_array = self.abs().ivy_array
         return self
+    
+    def bitwise_not(self, *, out=None):
+        return torch_frontend.bitwise_not(self._ivy_array)
 
     def bitwise_and(self, other):
         return torch_frontend.bitwise_and(self._ivy_array, other)
diff --git a/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py b/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py
index e231384b9553d..f6c56acd9f83a 100644
--- a/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py
+++ b/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py
@@ -2620,6 +2620,39 @@ def test_torch_instance_is_cuda(
     )
 
 
+# bitwise_not
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="bitwise_not",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("integer"),
+        num_arrays=2,
+    ),
+)
+def test_torch_instance_bitwise_not(
+    dtype_and_x,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    frontend,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        init_all_as_kwargs_np={
+            "data": x[0],
+        },
+        method_input_dtypes=input_dtype,
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        method_all_as_kwargs_np={
+        },
+        frontend=frontend,
+    )
+
+
 # bitwise_and
 @handle_frontend_method(
     class_tree=CLASS_TREE,