ML and IoT Based Smart Vehicle Health Monitoring and Predictive Maintenance System

Abstract: Maintaining modern vehicles in today’s fast-paced and technology-driven environment is increasingly complex, especially as vehicle systems grow more sophisticated. Traditional maintenance practices often rely on fixed intervals, which may lead to either premature servicing or delayed interventions, both of which can compromise vehicle performance and safety. This research proposes a ML & IoT Based smart car maintenance framework that leverages Machine Learning (ML) and Internet of Things (IoT) technologies to deliver a predictive and automated solution for vehicle health management. The primary focus is on monitoring and forecasting engine oil degradation, a critical factor in engine longevity and performance. IoT-enabled sensors embedded within the vehicle continuously collect real-time data such as mileage, engine temperature, oil viscosity, and driving patterns. This data is processed by an ML-based predictive model trained to accurately estimate the remaining useful life of the engine oil. Once predefined thresholds are reached, the system autonomously schedules a service appointment through a connected mobile application, ensuring timely and efficient maintenance without user intervention. By enabling condition-based maintenance, the system reduces unnecessary servicing, lowers operational costs, prevents mechanical failures, and enhances the overall driving experience. The proposed solution represents a significant step toward intelligent, connected, and sustainable automotive maintenance systems.