The maintenance of rail vehicles and infrastructure plays a critical role in reducing train delays, preventing malfunctions, and ensuring the economic efficiency of rail transportation companies. Predictive maintenance systems powered by supervised machine learning algorithms offer a promising approach by detecting potential failures before they occur, reducing unscheduled downtime, and improving operational efficiency. However, the success of such systems depends heavily on high-quality labeled data, necessitating user-centered labeling interfaces tailored to annotators’ needs for Usability and User Experience. This study introduces a cost-effective predictive maintenance system developed as part of the federally funded research project “DigiOnTrack,” which combines structure-borne noise measurement methods with supervised machine learning to provide monitoring and maintenance recommendations for rail vehicles and infrastructure in rural Germany. The system integrates wireless sensor networks, distributed ledger technology for secure data transfer, and a dockerized container infrastructure hosting the labeling interface and alarming dashboard. Train drivers and workshop foreman were annotators, labeling faults on rail infrastructure and vehicles to ensure accurate predictive maintenance recommendations. The Usability and User Experience evaluation revealed that the locomotive drivers’ interface achieved “Excellent Usability,” while the workshop foreman’s interface was rated as “Good Usability.” These results highlight the system’s potential for seamless integration into daily workflows, particularly regarding labeling efficiency. However, areas such as Perspicuity require further optimization for more data-intensive scenarios. The findings offer actionable insights into the design of predictive maintenance systems and labeling user interfaces, providing a foundation for future guidelines in Industry 4.0 applications, particularly in rail transportation.