This work presents the development and integration of predictive models for battery health monitoring in electric vehicles (EVs). It also identifies the applied machine learning and cloud computing technologies. Key targets are attracting the computational efficiency of state of charge (SoC) models, integrating cloud solutions for effective data management, and a relative comparison of studies concerning predictive maintenance approaches. Some of the latest literature studied are in relation to estimating SoC correctness using advanced algorithms, like SVM and ANN. Drew critical thinking into the limitations of BMS, obviously highlighting tough thermal management and standardized testing protocols. The results reflect the fact that predictive models effectively enhance the accuracy of SoC and SoH predictions, which means increased battery longevity and performance, by using data from historical analyses on battery performance. Stronger models are produced through addition of real-time data from IoT, with an immense drop in error of estimates with changing operational conditions. Furthermore, data management centralization through cloud integration will enable the ability to monitor real-time battery performance, which will help in invoking proactive maintenance strategies and attentiveness by operators regarding certain issues before they escalate. In conclusion, the research highlights that extrapolative maintenance strategies, informed by advanced analytics, are extra cost-efficient and reliable associated to traditional approaches. Despite these advancements, problems related to data quality and variability in the performance of batteries are still present and will require continuing research efforts to address these issues and enhance interoperability of various data systems as well as integrating machine learning with existing BMS. Such efforts will mainly play a prime role in the maintainable growth of the electric vehicle industry along with improving the reliability of BMS.