Modern railway systems face growing demands for enhanced safety, optimised maintenance, and environmentally responsible operations. Standard testing techniques, which involve conducting tests and inspections at intervals and following failures, do not provide the required real‐time data of the assets to avoid costly failures and derailments. To address this growing demand, this study presents a comprehensive conceptual framework for deploying IoT-based sensor networks in railways. The proposed framework features a multi-level structure. This structure encloses edge computing units, cloud-based analytics and user interfacing that enable constant monitoring and predictive maintenance. The various sensors which include vibration, acoustic, temperature, and environmental will send data to local edge nodes for first level analysis thereby reducing network load and delay. The cloud uses machine learning and other tools to find patterns in large amounts of data, predict equipment failures and schedule maintenance with the least disruption in services. Integration of strong security measures and redundancy minimizes cyber threats and hardware malfunctions. In addition to enhancing operational reliability, the proposed approach supports sustainability by reducing unscheduled maintenance trips and resource waste. Consequently, this framework provides a blueprint for railway operators, policymakers, and researchers to implement cutting-edge digital solutions that unify efficiency, safety, and environmental stewardship.