Wetland habitats, crucial ecosystems offering myriad environmental and economic benefits, are witnessing increasing vulnerabilities due to anthropogenic activities and natural disturbances. The development of infrastructure, such as railway lines, poses direct threats, leading to habitat fragmentation, changes in water presence frequency, and conversion of wetlands to agricultural lands or built-up areas. There is an urgent need for a comprehensive vulnerability assessment across distinct temporal phases to devise effective conservation strategies. The study aimed to assess the spatial variations of wetland vulnerability indicators across two phases: Phase-I (1988–2000) and Phase-II (2001–2018). The goal was to discern the direct impacts of infrastructural developments, like the introduction of a railway line, on wetland habitats and provide quantitative insights using machine learning models. Six datasets, namely, water presence frequency, fragmentation of wetland, percentage of wetland change, frequency of pixels being non-permanent, agricultural presence frequency, and NDBI, were integrated and analyzed. Four machine learning models—gradient boosting machine (GBM), random forest (RF), deep neural network (DNN), and artificial neural networks (ANN)—were employed to visualize wetland vulnerability across both phases. The models were subsequently validated using receiver operating characteristic (ROC) curves and Precision Recall curves. The analysis revealed that post-railway construction, areas with frequent water presence decreased by 14.98%, with an observed decline in patch and edge areas by 12% and 49%, respectively. A 14% increase was noted in areas undergoing more than 50% wetland conversion from Phase-I to Phase-II. Moreover, shallower wetland fringe areas were found to be most susceptible to vulnerability. The machine learning models offered varying vulnerability estimations; however, the deep neural network (DNN) model consistently showcased superior performance across both phases. Wetland habitats face increasing vulnerabilities due to human interventions, with infrastructural developments playing a significant role in their degradation. The comprehensive assessment provided by the machine learning models, especially the DNN, emphasizes the dire need for targeted conservation efforts and the relevance of employing such advanced modeling techniques for environmental monitoring and management.