It attempts to lower greenhouse gas emissions and combat global warming; modern society is undergoing a significant shift toward new sustainable energy generating and transportation methods. As long as research into efficient energy storage is ongoing, the energy storage sector has experienced substantial change. Despite advances in data assimilation techniques, the overwhelming volume and diversity of data, coupled with the lack of universally accepted models, underscore the limitations of these traditional approaches. Recently, deep learning has emerged as a highly effective tool in overcoming persistent issues in battery diagnostics by adeptly managing expansive design spaces and discerning intricate, multidimensional correlations. Because they offer inexpensive, high-power, and very efficient energy storage, batteries are indispensable. The lack of charging stations is one of the main problems EV manufacturers faces. In order to forecast how much charge EV batteries will have left; it suggests a battery management system. Among the techniques used are ensemble bagging, boosting, linear regression, SVM, ANN, and Gaussian process regression. It focuses on how artificial intelligence (AI) could promote efficient, eco-friendly travel and enhance EV battery management.