Predictive maintenance will be one of the mainstays of modern industrial systems, driven by developments in the Internet of Things, digital twin, and other intelligent sensing technologies. This paper presents a sensor-based predictive maintenance framework with a lightweight digital twin model for a conveyor belt system. The configuration includes load cells, current sensors, temperature sensors, and rotary encoders to track load fluctuations, motor health, thermal behavior, and movement of the belt. In real time, the data obtained from these sensors are processed using Python and mapped onto a 3D digital twin model created in Unity/Blender. The PdM process begins with gathering sensor data that is analyzed against pre-defined thresholds, indicating anomalies such as increased consumption of current, overheating, abnormal load, or fluctuations in speed. The maintenance score will be updated in real time, and when it reaches the critical threshold, alerts will be produced and recorded into a database for historical analysis. In order to allow remote monitoring, the system will send alert messages and show real-time sensor data, anomaly alerts on a user-friendly dashboard. It also provides on-site visual feedback through an LCD and mirror setup within a physical display box. In contrast to the current state of digital twins, which require proprietary cloud technology, expensive sensor networks and integration hardware to run, the offered framework is implemented as a low-cost, lightweight solution and can be scaled to support specific conveyor systems, including small and medium-sized ones, while still allowing real-time anomaly detection and visualization of maintenance predictions. The proposed strategy is cost-effective and scalable for small to medium-sized industrial environments with real-time sensing, predictive maintenance. It further offers digital twin visualization through both virtual and physical displays.