Predictive maintenance in the automotive sector has received a lot of attention because of its potential to improve operating efficiency and lower maintenance costs. This paper presents an approach for predictive maintenance that focuses on engine condition prediction and battery remaining usable life (RUL) predictions. The work intends to develop precise predictive models to detect anomalies in engine health and anticipate battery degeneration, allowing for proactive maintenance interventions. The research entails gathering and analyzing data from engine sensors and battery characteristics in order to train and test various machine learning models. XGBoost was the best algorithm for forecasting engine health, with an accuracy of 0.66, while Bagging Regressor was the best regression technique for engine prediction, with an MSE of 4443.04 and RMSE of 66.66. These models are highly accurate and efficient, demonstrating their potential for real-world use in automobile maintenance.