<p>Bridges that serve pedestrians must remain comfortable and safe under routine human‑induced and environmental loads, yet many lightweight footbridges receive minimal quantitative monitoring. This study aims to establish a cost‑effective workflow that combines ambient vibration testing with multi‑algorithm operational modal analysis and sensitivity‑driven finite‑element model updating to deliver an engineering‑grade dynamic model of a pedestrian bridge. Ten accelerometers recorded thirty‑minute ambient datasets; modal parameters were extracted using Frequency‑Domain Decomposition, Enhanced FDD, and Stochastic Subspace Identification, while a detailed finite‑element model was built and iteratively calibrated by adjusting mass density and stiffness to minimise frequency discrepancies and maximise mode‑shape correlation. The calibrated model reduced initial frequency mismatches of up to twenty‑1% to less than or equal to 1%, achieved Modal Assurance Criterion values above 90%, and met the three‑hertz vibration serviceability limit specified in EN 1991‑2:2003. Sensitivity analysis identified mass density and Young’s modulus as dominant parameters, providing practical guidance for future calibrations, and verification against closed‑form beam theory yielded an R‑squared of 0.99, confirming predictive fidelity. These findings demonstrate that a non‑destructive, field‑data‑driven approach can produce reliable dynamic characterisation for maintenance planning and condition assessment of under‑monitored footbridges. Future research should extend the framework to long‑term monitoring, incorporate damping and boundary‑condition updating, and apply machine‑learning techniques for real‑time diagnostics.</p>