Digital-twin (DT) technology provides a data-driven framework for reproducing complex coastal processes with high spatiotemporal fidelity. In this study, we develop an integrated geospatial digital-twin simulation system (DTSS) for the Suncheon Bay Wetland, an estuarine environment characterized by a pronounced S-shaped tidal channel. High-resolution observations, including sonar-based bathymetry, unmanned aerial vehicle photogrammetric digital elevation models, tidal elevations, current velocity profiles, conductivity temperature depth measurements, and total suspended material time series, were collected during dry (May 2024) and wet (July 2024) seasons and applied to hydrodynamic and salinity-dispersion simulations using the TELEMAC–MASCARET system. The model reproduced semidiurnal tidal behavior under baseflow conditions and captured monsoon-driven effects on river discharge, including delayed ebb–flood transitions and increases in velocity from <0.4 m/s to nearly 1.0 m/s. Salinity simulations revealed sharp gradients during normal flow and near-complete freshwater dominance (<0.1 psu) during peak discharge. Validation metrics [RMSE, BIAS, and Pearson correlation coefficient (R > 0.8)] demonstrated strong agreement between model outputs and observations for tidal elevation, velocity, salinity, and temperature. The DTSS framework effectively integrates sensor-based observations, geospatial data, and numerical modeling, demonstrating how in situ sensor networks and DT technologies can be combined to support real-time environmental monitoring, predictive simulations, and intelligent coastal management. These results underscore the importance of river–tide interactions and demonstrate the potential of sensor-driven DT technologies as an integrated platform for real-time wetland monitoring, environmental forecasting, and intelligent coastal management.