The primary goal of machine learning models in predictive maintenance is to foresee equipment failures before they occur. By analyzing historical data and identifying patterns, these models can predict when machinery is likely to malfunction. This predictive capability allows organizations to schedule maintenance proactively, reducing unexpected downtime and repair costs. As a result, businesses can optimize their maintenance schedules, improve overall operational efficiency, and extend the life of their equipment.