The automobile and transport industries have always tried to reduce downtime by the way of preventive maintenance. In the past decade, electromechanical sensors have become more accurate along with novel innovations in IoT and machine learning, and automobiles have leveraged this. In this paper, an end-to-end open-source predictive maintenance solution is presented to predict the severity of faults in a car using onboard historical and real-time sensor data using IoT and machine learning. Sensor data is collected from a Suzuki Swift VXi Model, and classifiers like logistic regression, random forest, and gradient boosting trees are used to train the data with imputed faults. F1 score and AUC are used as evaluation metrics. An end-to-end onboard diagnostics (OBD) data to the user dashboard pipeline is proposed with final predicted faults visible on a real-time dashboard. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.