Traditional energy management in HEVs poses problems such as reactive fault detection, inefficient power sharing, and the lack of online monitoring, hence leading to loss of energy and unexpected failure of the vehicle. Our proposed system integrates MEMS sensors for monitoring vehicle tilt and motion with adaptive speed control based on the terrain and road conditions. It makes use of a CNN-LSTM hybrid model for predictive anomaly detection; hence, the dynamic increase or decrease in motor speed according to the optimization needed in energy consumption aims at efficient power utilization and perfect safety and vehicles. The proposed hardware system fuses the information from five IoT sensors including temperature, vibration, distance, light intensity, and angle in order to monitor the health of the vehicle while minimizing energy consumption. Using the sensor data from the hardware system 4,000 multivariate time-series samples are collected. The collected data are trained with convolutional neural networks for spatial feature extraction and long short-term memory networks for temporal pattern extraction. Real-time prediction is achieved with processing times under 100 ms, and thus the system is suitable for on-road applications. The results show significant improvements over the conventional approaches, with an improvement of 15% accuracy over single-CNN implementations and a 25% reduction in false positive rates. This research advances intelligent transportation systems by presenting a reliable framework for predictive maintenance and energy optimization in hybrid vehicles.