A well-planned data classification system makes essential data for easy to use and finds and retrieves the data. At the point, when an individual considers buying a vehicle, there are many aspects that could impact his/her choice on which kind of vehicle he/she is interested in. Even if a brand new motor vehicle, it could be a failure in any component without maintaining and serviced a short period. Vehicle uptime is getting increasingly important as the transport solutions become more complex and the transport industry seeks new ways of being competitive. Traditional Fleet Management Systems are gradually extended with new features to improve reliability, such as better maintenance planning. Typical diagnostic and predictive maintenance methods require extensive experimentation and modeling during development. This paper investigates unsupervised and supervised methods for predicting vehicle maintenance. The methods rely on a telematics gateway that enables vehicles to communicate with a back-office system. These are later associated with the repair history and form a knowledge base that can be used to predict upcoming failures on other vehicles that show the same deviations. Data mining presents an opportunity to increase significantly the rate at which the volume of data can be turned into useful information. The purpose of the prediction algorithms is to forecast future values based on present records, in order to estimate the possibility of a machine break-down and therefore to support maintenance teams in planning appropriate maintenance interventions. It is observed that the proposed data mining approach provides promising results in predicting the exact value.