This research presents the development of an intelligent sensor-based monitoring system to enable predictive maintenance of railway electrical connections. The technical approach integrates Internet of Things (IoT) devices, edge analytics, and wireless sensor network technologies. A custom clamp-mounted sensor array with onboard processing provides real-time insights into connector thermal behavior. Through centralized analysis of data using modern big data techniques, railway operators gain visibility into early indications of interface degradation for enhanced decision-making. Compared to periodic inspections, findings demonstrate the proposed continuous condition monitoring system significantly improves fault detection response time. With a thorough examination of railway power supply challenges and state-of-the-art advancements in sensing and connectivity, this study highlights the tremendous potential of smart sensor infrastructures to transform railway safety, reliability, and efficiency via data-driven, predictive maintenance programs focused on electrical connector health. Widespread implementation promises to reduce costs and prevent disruptions from unexpected connection failures.