Technologies that enable predictive maintenance in manufacturing include IoT sensors, data analytics, and machine learning. IoT sensors track equipment conditions in real time, collecting data on parameters like temperature, vibration, and wear. This continuous monitoring generates large datasets that are analyzed using machine learning algorithms to identify patterns and predict potential failures before they occur. By leveraging these insights, manufacturers can schedule maintenance activities proactively, minimizing downtime and reducing costs while enhancing overall operational efficiency.