This paper investigates the concept of Digital Twin (DT) applied to the maintenance of railway transport networks, a revolutionary approach that exploits advanced technologies to improve reliability, safety and efficiency of these critical infrastructures. A digital twin is a dynamic virtual replica of a physical asset (a train, a track section, a station, etc.) or an entire system, constantly updated with real-time data from sensors. This replica allows the simulation, analysis, visualization, and optimization of the asset or system’s behavior in the real world. DT and predictive maintenance (PM) are two closely related concepts in Maintenance 4.0; DT is the advanced environment for performing PM in an optimum and comprehensive manner. Subsequently, PM is a major DT feature as it is fueled by DT data and context. Traditional maintenance, often reactive or preventive at fixed intervals, is restricted in its ability to proactively predict and prevent failures. Intelligent maintenance, conversely, integrates IoT sensors, Big Data analysis, Artificial Intelligence (AI) and machine learning to monitor the state of railway assets (track, rolling stock, signaling, etc.) in real time. This approach enables a shift from corrective maintenance to predictive maintenance, where abnormalities and failure warning signs are detected and assessed in order to schedule appropriate interventions before a breakdown occurs. The benefits are manifold: lower maintenance costs thanks to optimized interventions and fewer unplanned shutdowns, improved safety by preventing accidents due to equipment failures, and increased network availability and improved passenger and freight service quality. This report will cover the different technologies and methods involved in the DT maintenance of rail networks, such as remote condition monitoring, vibration analysis, infrared thermography, natural language processing for the analysis of maintenance reports, and failure prediction algorithms. It will also highlight the challenges involved in deploying these systems, including managing and integrating massive data, cybersecurity, and training staff in the new technologies. The findings in this paper have been derived from Alessandro Fantechi’s research on railway networks, focusing mainly on the application of formal methods for ensuring the safety, dependability and security of critical systems.