Estuarine systems are particularly vulnerable to climate change, as sea-level rise and altered river discharge drive inland saltwater intrusion. This intrusion disrupts biogeochemical cycles, ecosystems, and human activities, such as agriculture and industry, by increasing salinity levels. Addressing these challenges, we present a novel hybrid methodology combining advanced numerical modeling with statistical techniques to predict salinity behavior in estuaries. This methodology enables fast and robust predictions compared to other techniques currently in use. The approach begins with identifying seven key parameters influencing estuarine salinity, including tidal range, river discharge, and storm surges. Using Latin Hypercube Sampling (LHS), synthetic scenarios are generated, subsequently, representative cases are selected through the Maximum Dissimilarity Algorithm. Those selected cases are modeled using the Delft3d numerical model, calibrated and validated using field data collected from an estuary in northern Spain. Principal Component Analysis (PCA) is applied to reduce data dimensionality, identifying dominant salinity modes, followed by interpolation through Radial Basis Functions (RBFs) to create a interpolation surface. Results demonstrate that the hybrid methodology captures salinity concentration dynamics under diverse conditions, providing rapid and reliable predictions. This will enable the establishment of more efficient management systems to address different uses for industrial and agricultural activities in estuarine environments.