This study develops an 5D digital twin platform for bridge safety management, incorporating cable force assessment, structural response monitoring, and water level forecasting. The proposed platform combines real-time sensing, field testing, numerical simulation, and 5D safety monitoring platform to enable comprehensive bridge safety evaluation. This platform is applied to ShangGuishan Bridge. A Structural Health Monitoring (SHM) system is established, including sensors for temperature, strain, settlement, displacement, and cable force, with clearly defined inspection, alert, and action thresholds. To validate structural performance, static load testing and ambient vibration measurements are conducted. Static load results demonstrate that deflections at quarter-span, mid-span, and three-quarter-span locations are consistent with design expectations. Ambient vibration measurements successfully identify natural frequencies and mode shapes in longitudinal, transverse, and vertical directions. Finite Element Modelling (FEM) is developed to perform structural modal analysis, static analysis of cable structures, static vehicle load identification, and nonlinear seismic analysis by LS-DYNA. Simulation results show a high degree of agreement with the ambient vibration measurements. Cable analyses further demonstrate that severe cable failure significantly compromises structural safety and may lead to progressive rupture, emphasizing the importance of periodic inspection and maintenance. Seismic analyses using scaling earthquake ground motions reveal critical stress concentrations at the connections between the main girders and the end cross girder. Moreover, an Artificial Intelligence (AI)-based water level forecasting model is established to predict short-term river level variations during typhoons and heavy rainfall events, providing a reference for bridge early warning. Finally, a self-developed 5D digital twin platform is implemented, integrating 3D city reality model, real-time monitoring data, analytical results, and decision support systems. The proposed framework effectively enhances bridge safety management, early warning capability, and disaster response efficiency, providing a valuable reference for long-span bridge monitoring and resilience-oriented infrastructure management.