A life cycle of battery with long testing time and without contact measurement devices will be applicable for industrial applications. To this problem, the solution for potential will be provided by the combined technique of supervised learning and infrared thermography. The focus of the research will be on machine learning techniques. Some high accuracy machine learning algorithms used in conjunction with thermography to estimate the life cycle of lithium ion polymer batteries. The capturing of infrared images charging of curtain minutes followed by the discharging of 60 minutes for respective cycles. For machine learning and deep learning models the input nodes of charging or discharging use the surface temperature profiles. The input will be in thermal profile for the result. Finally to compare both machine learning and deep learning to show the result based on accuracy score of the system.