This chapter focused on modeling theWetland Hydrological Strength (WHS) hydrological strength of wetlandsWetland using various machine learningMachine Learning (ML) algorithms, including the reduced error pruning tree (REPTreeReduced Error Pruning Tree (REPTree)), artificial neural network (ANNArtificial neural network (ANN)), and random forest (RFRandom Forest (RF)). The analysis incorporated key hydrological indicators such as water presence consistency, water depth, and hydro-periodHydro-Period (HP) to evaluate wetlandWetland functionality. By comparing modeled hydrological conditions before and after damDam construction, the study aimed to quantify the impact of regulation on wetlandWetland water dynamics. Results from parameter-based analysis indicated a substantial decline in wetlandWetland categories following damming. Specifically, areas classified as perennial wetlandsWetland decreased by approximately 22%, while semi-perennial wetlandsWetland declined by around 47%. A particularly notable reduction was observed in post-monsoon high water presence zones, which dropped sharply from 467 km2 to just 40.34 km2. Among the machine learningMachine Learning (ML) models employed, all demonstrated reliable performance when compared with observed field conditions. However, the REPTreeReduced Error Pruning Tree (REPTree) model emerged as the most accurate. According to its outputs, the proportion of the area classified under high hydrological strength decreased from 46.61% in the pre-dam period to 31.76% after regulation. This reduction was especially pronounced along the fringe zones of wetlandWetland ecosystems, indicating a spatially uneven decline in hydrological resilience due to damDam-induced flow alterationsFlow alteration.