Activation functions are essential in neural network-based models, introducing non-linearity that allows neural networks to learn complex patterns. Traditional functions like Sigmoid, Tansig, and ReLU have limitations, such as vanishing gradients and non-differentiability, which can slow performance. Fractional calculus offers a promising alternative using non-integer order derivatives to enhance activation functions. These fractional-based functions improve the performance of neural networks in classification and regression tasks, addressing the shortcomings of conventional methods. Furthermore, fractional calculus has applications in system modeling, control theory, and signal processing, making it a versatile tool for managing nonlinear dynamics across various engineering and scientific fields.