The article shows how condition-based maintenance (CBM) and digital twins can improve the reliability and performance of conveyor belt systems in process industries, especially mining. By modeling the full system, including sensors, control systems, and operational data, the researchers could predict component degradation and estimate the remaining useful life (RUL) of key parts, such as head drum bearings. This proactive, data‑driven approach helps reduce downtime, improve overall equipment effectiveness (OEE), and support better maintenance planning. The study concludes that successful optimization depends on good data availability, well‑defined KPIs, and integrating multiple data sources into a unified monitoring system.