diff --git a/neural_network/activation_functions/softsign.py b/neural_network/activation_functions/softsign.py
new file mode 100644
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+++ b/neural_network/activation_functions/softsign.py
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+"""
+Softsign activation function
+
+Use Case: Softsign provides a smooth transition without sharp gradients
+and is an alternative to sigmoid functions.
+
+For more detailed information, you can refer to the following link:
+https://paperswithcode.com/method/softsign-activation
+"""
+
+import numpy as np
+
+
+def softsign(vector: np.ndarray) -> np.ndarray:
+    """
+    Implements the Softsign Activation Function.
+
+    Parameters:
+        vector (np.ndarray): The input array for Softsign activation.
+
+    Returns:
+        np.ndarray: The output after applying Softsign activation.
+
+    Formula: f(x) = x / (1 + |x|)
+
+    Examples:
+    >>> softsign(np.array([-10, -5, -1, 0 ,1 ,5 ,10]))
+    array([-0.90909091, -0.83333333, -0.5       ,  0.        ,  0.5       ,
+            0.83333333,  0.90909091])
+
+    >>> softsign(np.array([100]))
+    array([0.99009901])
+
+    """
+    return vector / (1 + np.abs(vector))
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()