WetlandsWetland areModeling wetland health ecologically sensitive systems that require robust assessment methodologies to quantify their health and degradation. This study tries to develop novel wetlandWetland health parameters by incorporating multi-sensor remote sensing datasets, integrating hydrological, thermal, and vegetation indices related indicators of wetlandWetland stability and degradation. Traditional machine learning (MLMachine Learning (ML)) algorithms (SVM, LRLogistic Regression (LR), RFRandom Forest (RF), DTDecision Tree (DT), GBM, XGBExtreme Gradient Boosting (XGB), LGBMLight GBM (LGBM)) and advanced deep learning (DLDeep Learning (DL)) models (ANNArtificial neural network (ANN), DNN, CNNConvolutional Neural Networks (CNNs), WDNNWide & Deep Neural Network (WDNN), SNNSelf-Normalizing Neural Network (SNN)) were applied to assess wetlandWetland health before/pre- and after/post-dam construction periods. Model performanceModel performance was assessed using different precision measures including recall, precision, F1-score, MCCMatthews Correlation Coefficient (MCC), ROCReceiver Operating Characteristics (RoC)-AUC, and Cohen’s Kappa to ensure comprehensive assessment. The highest accuracy in pre-dam was observed in RFRandom Forest (RF) (0.848), XGBExtreme Gradient Boosting (XGB) (0.850), and SNNSelf-Normalizing Neural Network (SNN) (0.827), while in post-dam accuracy declined in all the models, with RFRandom Forest (RF) (0.828), XGBExtreme Gradient Boosting (XGB) (0.830), and SNNSelf-Normalizing Neural Network (SNN) (0.805) remaining top performers. The post-dam period exhibited increased wetlandWetland degradation, with the “very poor” class expanding significantly. Pre-dam estimated the highest at 219.06 km2 (DTDecision Tree (DT)), while in post-dam prediction, it was found 169.50 km2 (LGBMLight GBM (LGBM)) for MLMachine Learning (ML) models (Table 1, Table 3) and 135.94 km2 (WDNNWide & Deep Neural Network (WDNN)) for DLDeep Learning (DL) models, indicating severe ecological decline. Concurrently, the “very good” wetlandWetland zones shrank considerably—from a pre-dam high of 539.43 km2 (LGBMLight GBM (LGBM)) to a post-dam maximum of only 128.05 km2 (XGBExtreme Gradient Boosting (XGB)) in MLMachine Learning (ML) predictions, and from 273.31 km2 (WDNNWide & Deep Neural Network (WDNN)) to just 94.82 km2 (ANNArtificial neural network (ANN)) in DLDeep Learning (DL) models. These trends reflect a loss of wetlandWetland functionality and ecosystem integrity due to hydrological disruption. Explainable AI (XAIExplainable AI (XAI)) analysis identified hydrological connectivity (HCSHydrological Connectivity Strength (HCS)), water persistence (WPFIWater Persistence & Frequency Index (WPFI)), and water stability (WEIWater Entropy Index (WEI)) as primary indicators of resilience in the pre-dam scenario. In post-dam, dominant drivers shifted to anthropogenic stress (AESIAquatic Ecosystem Stability Index (AESI)), vegetation degradation (AVDIAquatic Vegetation Degradation Index (AVDI)), and thermal stress (TWSIThermal-Water Stress Index (TWSI)), indicating a transition from hydrological resilience to stress-induced deterioration. These findings underscore the adverse consequences of damDam-induced hydrological alterationsHydrological alteration on wetlandWetland health and emphasize the urgent requirement for targeted protection strategies. Priorities should include restoration of hydrological connectivity, mitigation of vegetation loss, and control of thermal and anthropogenic pressures. The integration of MLMachine Learning (ML), DLDeep Learning (DL), and XAIExplainable AI (XAI) provides a powerful framework for continuous wetlandWetland monitoring, supporting informed, data-driven policy for sustainable wetlandWetland management in human-impacted environments.