The widespread use of Electric Vehicles (EVs) has highlighted the vital requirement for reliable and precise fault diagnosis systems for their complex battery packs. Identifying and recognizing faults in the battery system early is imperative to ensure the safety, dependability, and performance of electric vehicles. An enhanced EV battery problem diagnosis approach is presented in this abstract in full. Modern data analytics and machine learning approaches are combined in the suggested methodology to offer a methodical and efficient way to identify battery problems. To create an accurate model of typical battery behavior, it incorporates historical data together with data from multiple sensors, including voltage, current, and temperature. It is thus possible to identify deviations from this model as possible flaws. A variety of battery issues, including as cell imbalance, thermal runaway, and deterioration, can be found by the diagnostic system. It ensures timely action to prevent potential safety issues by providing real-time monitoring and diagnosing capabilities. Furthermore, the system can continuously learn and adapt, which enables it to adjust to different EV models and battery chemistries. Real-world data from a variety of EVs and rigorous testing have been used to validate the presented approach. High accuracy in fault detection is demonstrated by the results, allowing for prompt maintenance and repair measures. An efficient fault diagnosis system is essential to maintaining the longevity and safety of electric vehicles (EVs) as their adoption grows. This study advances the field of EV battery diagnostics and marks a significant milestone in the ongoing development of dependable and sustainable electric vehicles.