Anomaly detection is essential for identifying irregular patterns that may indicate system deterioration, component failure, or defects. By leveraging machine learning, it enables continuous condition monitoring, integrating historical data with real-time trends to enhance reliability, reduce downtime, and support proactive maintenance. This chapter explores vehicle health monitoring and early fault diagnosis using anomaly detection methods. Three methods are proposed for failure prediction: two employ Mahalanobis distance after pre-processing and feature engineering, while the third utilizes an Autoencoder neural network with inbuilt feature learning capabilities. Each approach establishes thresholds based on healthy vehicle data, detecting anomalies when deviations occur. The implementation has been carried out using Python programming language.