The electrification of transportation has spurred significant interest in improving the performance and efficiency of electric vehicle (EV) batteries. Predictive modelling techniques, such as Artificial Neural Networks (ANN), offer a promising approach to analyze and predict EV battery performance. This study presents a comprehensive analysis of EV battery performance using ANN, focusing on key performance metrics such as capacity, charging time, and efficiency. The study utilizes a dataset comprising historical data collected from EV batteries, including factors such as temperature, charging rate, and battery age. The ANN model is trained using this dataset to predict battery performance in various settings. The outcome demonstrates Considering the ANN model can accurately predict battery performance metrics, with high levels of accuracy and reliability. Furthermore, the study investigates the impact of various factors on battery performance. It is found that temperature has a significant influence on battery capacity and charging time, with higher temperatures leading to reduced capacity and longer charging times. Similarly, charging rate is found to impact battery efficiency, with higher charging rates leading to lower efficiency. The study also explores the potential use of the ANN model for optimizing battery performance. By analysing the ANN predictions, it is possible to identify optimal operating conditions for EV batteries, such as temperature and charging rate, to maximize performance and efficiency. Overall, this study highlights the effectiveness of using ANN for predictive analysis of EV battery performance. The results demonstrate the potential of ANN as a valuable tool for battery manufacturers and EV manufacturers to optimize battery design and operation. By leveraging ANN models, manufacturers can improve battery performance, increase efficiency, and enhance the overall driving experience for EV owners.