AI-driven predictive maintenance in manufacturing is a cutting-edge approach that uses artificial intelligence (AI) to predict equipment failures and perform maintenance at the right time. This method is in contrast to traditional maintenance strategies such as reactive maintenance (repairing machines after they break down) and preventive maintenance (planned maintenance based on the average life of the equipment). AI-driven predictive maintenance in manufacturing significantly increases operational efficiency by reducing unplanned equipment downtime and extending machine life. It enables cost-effective resource allocation by allowing maintenance to be performed exactly when needed, based on AI’s accurate predictions of potential failures. This approach not only increases overall productivity, but also increases safety in the production environment by preventing equipment failures before they occur. In this study, we explore the intricacies of AI-driven predictive maintenance by examining its basic principles, methodologies, and the effective role of machine learning and data analytics in predicting equipment failures. We then present a comprehensive case study using an open-source dataset to demonstrate the practical application and effectiveness of these AI techniques in a real-world manufacturing scenario. This case study not only demonstrates the data collection and analysis process, but also reveals the concrete benefits and challenges encountered in implementing AI-driven predictive maintenance strategies.