Transportation companies must face to a huge competition and must reduce downtime and the associated costs. This can be achieved through predictive maintenance (PM), which defines maintenance actions based on the health of the system and its environment. Relevant information can be extracted from massive data related to health prognosis and management (PHM) by applying artificial intelligence (AI) techniques. This paper proposes a Machine Learning approach to develop a prediction model based on a supervised learning by comparing several regression algorithms. The model is then applied to the Remaining useful mileage prediction of trucks tires for a transport application of dangerous substances.