The Automobile Life Extender (ALE) comprises an on-board function, a machine learning model operating via cloud computing and a smartphone app. The on-board function receives signals such as engine RPM, throttle position, brake pedal position, and hydraulic pressure from the vehicle's ECUs. Based on this data, the on-board ALE module calculates the engine load, brake circuit load, etc., and sends it to the predictive maintenance model via the on-board IoT system. The predictive maintenance model contains recorded data about the type of engine, brake system, and their performance curves acquired from tests conducted by its OEM. Machine learning models holds a crucial role in dynamically analyzing vehicle data, identifying drive patterns, and predicting the need for maintenance of a part or system. A hybrid approach of training models based on supervised and unsupervised learning is incorporated, creating an active learning strategy to maximize the use of available data. Amazon SageMaker handles training the ML models, which can be fed into the Amazon Bedrock cloud agent as a customized model. The Amazon Bedrock code interpretation feature assists in visualizing the machine learning model. The model predicts whether repair, replacement, or maintenance is needed. The results from the ML model are displayed to the driver via a smartphone app. Based on the owner's approval, the service center can access the results from the cloud to perform diagnostics even before the vehicle reaches the service station and allocate servicing slots based on their current workload, spare parts availability, and the vehicle owner's schedule. The modular design approach is applied to accommodate other vehicle types, with provisions for model retraining based on new data.