Failures are the eminent aspect of any sophisticated machine such as vehicles. Early detection of faults and prioritized maintenance is a necessity of vehicle manufacturers as it enables them to reduce maintenance costs, safety risks and increase customer satisfaction. In this study, we propose to use a type of Ant Colony Optimization (ACO) algorithm to diagnose vehicles faults. We explore the effectiveness of ACO for solving fault detection in the form of a classification problem, which would be used for predictive maintenance by the manufacturers. We show experimental evaluations on the real data captured from heavy-duty trucks illustrating how optimization algorithms can be used as a classification approach to forecast component failures in the context of predictive maintenance © ESREL 2021