Amid the increasing concerns of global climate change, the Brahmani River Basin in India stands out as a significant hydrological system undergoing notable variations in rainfall patterns. Utilizing the Adaptive Neuro-Fuzzy Inference System (ANFIS), this study accurately forecasted these rainfall patterns, achieving significant closeness during the training and testing phases. Subsequently, these projections were incorporated into the North American Mesoscale (NAM) and the Variable Infiltration Capacity (VIC) models. Notably, the VIC model demonstrated robustness with high R-values both during the training and testing phases. Using this forecast data, a comprehensive water budget analysis was performed for the Gomlai and Santrabandha micro-catchment, revealing a discerning trend of declining water discharge at the Gomlai station. This assessment underscored a pronounced water demand-supply discrepancy in the Gomlai and Santrabandha micro-catchments, highlighted by a shortfall of 1.74 Mm³ and a surplus of 0.46 Mm³ annually in the respected micro watersheds. Additionally, the Analytical Hierarchy Process (AHP) was employed to delineate the spatial hydrological dynamics of the basin, which identified the specific discharge and recharge zones. Collectively, the findings elucidate the hydrological implications of climate change on the Brahmani River Basin, proposing pertinent interventions and strategies. This research not only emphasizes the challenges posed to the Brahmani River Basin but also offers a roadmap for sustainable water management, showcasing its potential applicability in similar global contexts.