The railway industry relies heavily on the efficient operation of its infrastructure to facilitate the transportation of goods and passengers over long distances. In the last decades, wayside monitoring systems have emerged as crucial tools for ensuring the safety, reliability, and optimal performance of railway vehicles. This article investigates the evolving role of wayside monitoring, particularly focusing on the utilization of hot-box and hot-wheel detectors for proactive maintenance strategies. Traditional approaches to hot-box monitoring have been reactive, primarily focusing on detecting critical states of vehicles. However, a shift towards predictive maintenance using these classical systems may still be feasible by analysing deeply the detector data and extracting insights into the condition of bearings. The methodology involves reorganizing and redefining HB/HW data to identify anomalies indicative of changes in bearing operation or condition. Moreover, by assessing the quality of detector data and implementing adaptive thresholding and anomaly detection algorithms, false alarms and false negatives can be minimized, enhancing the efficiency of maintenance operations, and improving the reliability of railway networks. Overall, this study investigates and highlights the potential of utilising classical wayside monitoring systems to improve railway maintenance practices and contributing to safer and more efficient railway operations.