Purpose: To assess the impact of technical diagnostics and monitoring (TDM) systems on the recoverability of railway automation and telemechanic (RAT) devices. To investigate the shortcomings of modern diagnostic technologies with regard to their impact on increased operational costs and transportation safety risks. To justify the necessity of monitoring system modernization based on predictive analytics for transition from scheduled preventive maintenance (SPM) to intelligent methods of equipment condition management. Methods: Analysis of the successful implementation of domestic systems, such as the hardware and software complex of dispatching control (HSC DC), including their functionalities for data collection, pre-failure state detection, and automated fault identification. A study of graphical data reflecting the dynamics of rail circuit parameters for identifying deviations from the standard operation preceding failures. Comparing traditional SPM methods with modern approaches that are based on predictive analytics and machine learning. Results: A number of significant deficiencies in current monitoring systems have been identified. These include the occurrence of false alarms and inadequate performance in handling current workloads. The capacity of predictive analytics to detect subtle parameter changes, such as voltage fluctuations and temperature deviations, has been demonstrated to be indicative of developing pre-failure conditions. As demonstrated by the rail circuit graphs, the typical phases can be observed in the form of steady operation and the appearance of pre-failure anomalies, as well as failure states. Practical significance: The feasibility of enhancing TDM through the implementation of predictive analytics and digital modelling was demonstrated, with the potential to help minimize emergencies, optimize maintenance via targeted repairs, and reduce operational costs. The modernization of RAT systems is imperative for enhancing the reliability and safety of railway infrastructure, particularly in the context of ncreasing freight traffic and operational intensity. The findings of the study can be used to develop early failure warning systems and update regulatory frameworks in technical diagnostics.