This thesis presents an innovative approach for predicting turbocharger failures in mining haul trucks, leveraging the power of machine learning algorithms to improve predictive maintenance strategies within the mining industry. The haul trucks have a critical role in mining operations, and a turbocharger failure within the haul truck can cause significant costs. This research aims to minimize downtime, improve safety, and enhance operational efficiency. By incorporating machine learning models, this thesis introduces a novel predictive maintenance framework to monitor equipment conditions in real-time and identifies patterns that precede failures by analyzing sensor data from turbochargers, enabling preemptive maintenance actions. The study thoroughly examines turbocharger technology, common failure causes, and the role of various components. It also provides a detailed review of machine learning techniques applied to predictive maintenance such as ensemble learning, support vector machines, neural networks, and advanced anomaly detection methods. Two case studies form the core of the analysis, demonstrating how supervised and unsupervised learning models are applied for predicting failures and detecting anomalies. These models make use of sensor data to forecast the behavior of turbocharger systems, detecting both anomalies and potential failures. This approach allows for timely maintenance decisions, reducing unnecessary maintenance operations and preventing catastrophic failures. The thesis makes several contributions to the field, such as the use of machine learning models for predicting turbocharger failures, the development of new metrics for monitoring turbocharger health, and the identification of key sensors that affect turbocharger performance.