Battery Management System is a system that maintains a pack of cells which can utilize the actual life time of batteries used in a particular system. The main objective of this concept is to monitor, maintain and control each individual cells in a battery pack. Machine Learning (ML) has emerged to tackle complicated computational real-life problems that were previously difficult due to advancements in digitalization and the availability of dependable sources of information that supply credible data. Machine Learning plays an important role in predicting the life time of individual cells with respect to the performance of the battery pack. We can design a system which can be inbuilt with in the battery pack or outside it.Normally, a battery pack contains more than thousands of cells arranged in it. In this system, each cell is connected in a measuring circuit which measures the performance-based parameters. These parameters are monitored by a controller unit attached to the system. Based on the parameter check, the output voltage can be regulated and made constant, so that we can design a highly efficient system with more battery life. An inbuilt alert mechanism can alert the user if any of the cell value goes beyond an optimum level. If we add this system to a battery pack the performance and battery life can be increased up to few percentages.Machine learning algorithms can be integrated with the system to predict the live performance and the remaining life time of batteries. Live parameters from the system can be trained to predict the battery life and are provided as test data for training. The test data is stored and used as a log file to analyze the system. Multiple algorithms are used for training the data and the most accurate one is selected for prediction. The live data processing helps to reduce the complexity of the ML system.The system can be designed more flexible to integrate with any emerging technologies. For years researchers have tried to predict how many charging cycles a battery will last before it dies. Better predictions would enable more accurate quality assessment and improve long-term planning. The above system improves battery performance, safety, quicker charging, lifespan, and durability. One area of new thought is the use of machine learning (ML) as a facilitator in battery management systems (BMS).