This study presents a comprehensive approach to predicting the life span of supercapacitors using machine learning techniques, using input variables such as voltage, current, temperature, and capacity retention. This study uses machine learning algorithms like SVR (Support Vector Machines), linear regression, neural networks, and decision trees to predict the remaining useful life (RUL). The training data set employed for this was taken from various industrial applications. Quantitative matrices, including mean absolute error (MAE), root mean square error (RMSE), and R-squared, were applied to evaluate the performance of all models. Based on the evaluation, the proposed machine learning algorithms are providing a more accurate prediction of capacitor degradation, that can improve the maintenance and downtime of the supercapacitors in industrial processes. This study further helps to increase the lifetime of capacitors as well as make energy management more efficient, which helps the industries in the timely maintenance and replacement of the capacitors.