An overview of the crucial function that state-of-charge (SOC) estimate plays in Battery Management Systems (BMS) for electric vehicles (EVs) is given in the abstract. By tracking the amount of energy that is available, SOC estimate is crucial for guaranteeing effective and secure operation. This paper examines several ways that have been developed for SOC estimation, including as data-driven, model-based, and direct approaches, each with its own advantages and disadvantages. Model-based approaches strike a compromise between accuracy and complexity, but they need accurate battery models, whereas data-driven approaches rely on large training datasets. Despite difficulties in real-time application, emerging technologies like artificial intelligence algorithms and Kalman filters are improving the accuracy of SOC estimation. The paper also examines state-of-health (SOH) assessment techniques that concentrate on battery performance and longevity, ranging from data-driven methods to electrochemical models. It is expected that forthcoming developments in cloud-based BMS systems and smart BMS applications would surmount present computational constraints and enhance battery management capabilities in electric vehicles.