Load forecasting is the estimation of future electrical load and it is carried out to optimize the power system's efficiency and maintain a balance between supply and demand. The primary benefit is that it helps prevent either an excess or a shortage of electricity. Moreover, it lowers operating expenses and guards against blackouts. Electric load forecasting has been done using a variety of techniques, including machine learning and traditional techniques. Classifications for machine learning methods include neural networks, while classifications for traditional methods significantly include regression analysis, exponential smoothing and other enhanced algorithms. In order to examine the neural network's performance, we will be adjusting many parameters in this project, including the number of hidden layers, the number of epochs, the kind of method, and the kind of axons.