With the increase in the speed of development, there has been a dire need to make the development as efficient as possible. Also, the increased depletion of gasoline resources has forced automobile industries to switch to electric vehicles [1]. However, the switch is not so easy as the batteries used in the electric vehicles are not as good at times giving rise to dying down, wearing out, and even fire breakout without any prior intimation. In this paper, we are trying to solve that problem for electric vehicles and make a detailed study of the automated methods to check for the lithium-ion batteries that are widely used in the vehicles. In this paper, we will be discussing the implementation of machine learning in the domain and solving the problem of EV batteries [2] and helping to generate a smart system to check for the health and the state of charge of the lithium-ion batteries. With the use of machine learning algorithms like XG-Boost, Random Forest classifier, time series analysis algorithms and Deep learning algorithms like RCNN and deep LSTM provide an ensembled result output for the same. The accuracy of the projected module is expected to be much more than the existing systems.