Accurately predicting energy demand is crucial for managing charging infrastructure, maximising vehicle performance, and guaranteeing effective energy distribution as EV adoption picks up speed. This study offers a thorough and organised analysis of EV energy demand prediction methods, covering deep learning frameworks, machine learning models, and conventional statistical methods. It also presents a useful implementation using a web application built with Flask that forecasts EV energy use depending on variables like speed, temperature, battery capacity, and distance travelled. In order to provide real-time, easily navigable predictions, the system combines a learnt machine learning regressor with a data scaler. The entire pipeline—data preprocessing, model training, application design, and performance evaluation—is described in this paper, providing theoretical understanding and a practical solution for EV energy demand forecasting.