<p>Finite element model updating is essential for reliable train–bridge coupled analysis because the bridge model should reproduce both static and dynamic structural characteristics. However, updating based on a single class of responses may lead to inconsistent estimates of structural stiffness and mass. This study proposes a surrogate-assisted static–dynamic synergistic updating framework for a long-span railway suspension bridge. Three temperature-deflection slopes and four modal frequencies are adopted as the static and dynamic updating objectives, respectively. After comparing radial basis function (RBF), Kriging, and support vector machine surrogate models, the RBF model is selected for subsequent analysis. Sobol sensitivity analysis reduces the number of candidate parameters from 11 to 4, and a reduced parameter RBF surrogate model is integrated with NSGA-III and TOPSIS to identify a preferred compromise solution from the Pareto solution set. The proposed framework is evaluated using the Wufengshan Yangtze River Bridge. After updating, the slope errors are reduced to within 2.5%, the errors of the two symmetric modal frequencies are reduced to below 1%, and those of the antisymmetric modal frequencies are reduced to approximately 4%. The diagonal MAC values of the first four modes exceed 0.90. Under the investigated operating condition, the Static-only and Synergistic models produce similar thermal deformation profiles but different bridge and vehicle dynamic response levels, while all evaluated running-safety and ride-comfort indices remain within the adopted limits. These results indicate that modal-frequency constraints can influence the predicted dynamic responses and estimated safety margins. The proposed framework provides a practical approach for establishing a baseline finite element model with improved consistency in both static and dynamic characteristics.</p>