Predictive maintenance in automotive telematics signifies a revolutionary method for vehicle health management, using machine learning methods to foresee breakdowns and enhance maintenance schedules. This research utilizes machine learning methods to ascertain the loading status of trucks—loaded or empty—exclusively using data from the vehicle's communication network, particularly from the engine module. We attained an accuracy over 85% for small hauls (0.5 to 5 km) and approximately 95% for long hauls (5 to 500 km). This method optimizes fleet management by minimizing communication between managers and drivers, while also significantly contributing to research on fuel consumption reduction and advanced fault diagnostics. The findings demonstrate that machine learning-based predictive maintenance decreases unplanned downtime and maintenance expenses while also improving vehicle safety and durability. This paper provides a thorough examination of the efficacy of machine learning models in predictive maintenance, delineates the challenges associated with data privacy, computational efficiency, and integration with current automotive systems, and explores future avenues for creating more resilient and scalable predictive maintenance frameworks in the automotive sector.