Battery management systems (BMS) play a vital role in the safety, efficiency, and longevity of electric vehicles (EVs). As electric mobility increases, the BMS has a critical impact on enhancing the overall vehicle performance and energy management, making its optimization vital. In this regard, this paper presents how advanced artificial intelligence (AI) algorithms involving machine learning (ML), deep learning (DL), and reinforcement learning (RL) can overcome the major issues confronting BMS: precise state-of-charge (SOC) estimation, state-of-health (SOH) prediction, thermal management, and charge–discharge efficiency. AI approaches such as XGBoost and CatBoost achieve high00 performance for SOC and SOH predictions, with metrics like MAE, RMSE, and R2 reaching values of 2.243, 3.2, 0.99, and 17.1, 23.97, 0.99, respectively, showcasing the potential for superior accuracy and robustness. The integration of AI systems facilitates improved adaptability and intelligent energy distribution, propelling the journey toward a sustainable and efficient electric vehicle landscape.