This study presents an integrated Structural Health Monitoring (SHM) methodology designed to enable predictive maintenance of existing bridges through continuous real-time data acquisition and analysis. The proposed system employs a non-intrusive sensor network that measures key structural responses – such as vibrations, displacements, modal frequencies and damping ratios – without interfering with normal bridge operation. Acquired data are processed within a dedicated computational platform that converts raw measurements into diagnostic indicators and automatically issues alerts when predefined thresholds are exceeded. For each monitored bridge, a customized instrumentation layout is developed according to its geometry, typology, and boundary conditions. The collected data support the creation and calibration of a Digital Twin of the structure, refined through iterative correlation between experimental and numerical dynamic properties. Once calibrated, this Digital Twin allows predictive simulations, performance assessment, and failure forecasting under various loading scenarios. By integrating real-time sensing, numerical modelling and automated diagnostics, the proposed SHM framework provides a reliable, non-destructive and proactive tool for optimizing maintenance strategies, enhancing structural safety and extending bridge service life.