<p>Battery technologies, a crucial element of contemporary energy storage systems, have extensive use in several industries including electric cars, portable gadgets, and grid storage. The identification of the different problems associated with batteries is critical to ensure their reliability, performance, and safety. Traditionally, many techniques rely on hardware-based solutions and form the basis of early battery management systems (BMS), which have significant limitations concerning accuracy, flexibility, real-time operation, and scalability. These deficiencies call for more advanced, data-based methods, such as machine learning, which significantly raise the performance in fault detection and reliability while reducing costs. This paper reviews the progress in battery technology and fault detection techniques with emphasis on the transformational role of machine learning (ML) in enhancing the capabilities of the battery management system. The machine learning methods raise the accuracy of fault detection and provide a means for constructing a safe and dependable battery system for many applications.</p>