Electric Vehicle (EV) owners are increasingly adopting solar panels at home, highlighting a growing trend towards integrating renewable energy solutions into sustainable lifestyles. This research aims to advance electric vehicle (EV) technology and market strategies through predictive modeling. Focusing on battery efficiency, solar panel adoption among EV owners, and sales forecasting in emerging markets like Kerala, India. The study employs advanced statistical and machine learning techniques. Key objectives of the research include developing robust models to predict battery efficiency based on dataset features, forecasting solar panel adoption using customer data, and conducting comprehensive time series analysis of EV sales. For battery efficiency prediction, models such as Linear Regression, Gradient Boosting, and Random Forest are utilized and Linear Regression found most effective with least MSE of 0.00977. Logistic Regression, Random Forest, and Gradient Boosting models are employed to forecast solar panel adoption among EV owners. The analysis exhibited Logistic Regression with an accuracy of 79 percent as the best predictor with balanced precision and recall. Sales forecasting of EVs using time series analysis employs ARIMA and SARIMA models demonstrating the effectiveness of SARIMA with lesser error values as compared to ARIMA.