diff --git a/content/pytorch/concepts/tensor-operations/terms/bitwise-and/bitwise-and.md b/content/pytorch/concepts/tensor-operations/terms/bitwise-and/bitwise-and.md
new file mode 100644
index 00000000000..bc1ecd96b5f
--- /dev/null
+++ b/content/pytorch/concepts/tensor-operations/terms/bitwise-and/bitwise-and.md
@@ -0,0 +1,58 @@
+---
+Title: '.bitwise_and()'
+Description: 'Performs the element-wise bitwise AND operation on tensors in PyTorch.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags:
+  - 'Deep Learning'
+  - 'Methods'
+  - 'Programming'
+  - 'PyTorch'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'paths/data-science'
+---
+
+The **`.bitwise_and()`** method in PyTorch performs an element-wise bitwise AND operation on two [tensors](https://www.codecademy.com/resources/docs/pytorch/tensors) or a tensor and a scalar. It returns a new tensor containing `1` where both corresponding bits are `1`, and `0` otherwise. This method is useful for binary operations and low-level bit manipulation.
+
+## Syntax
+
+```pseudo
+torch.bitwise_and(input, other, *, out=None) → Tensor
+```
+
+**Parameters:**
+
+- `input`: The first input tensor.
+- `other`: The second input tensor.
+- `out` (Optional): The output tensor to store the result.
+
+**Return value:**
+
+Returns a new tensor containing element-wise bitwise AND values of the input tensors.
+
+## Example
+
+The following example demonstrates the usage of the `.bitwise_and()` method:
+
+```py
+import torch
+
+# Define two tensors
+a = torch.tensor([5, 6, 7])  # Binary: [101, 110, 111]
+b = torch.tensor([3, 2, 1])  # Binary: [011, 010, 001]
+
+# Perform bitwise AND operation
+result = torch.bitwise_and(a, b)
+
+print("Bitwise AND Result:")
+print(result)
+```
+
+The above code produces the following output:
+
+```shell
+Bitwise AND Result:
+tensor([1, 2, 1])
+```