The Indus Basin is facing water scarcity intensified by a drier climate and growing water demands for irrigation and population consumption. Ensuring environmental sustainability and resilience in the Indus Cascade necessitates a comprehensive understanding of human activities and climate influences on terrestrial water storage and groundwater depletion. The primary goal of this research is to promote food and water security in Indus Basin by providing place-based water management solutions. To do so, we used GRACE data, spatial downscaling, hydrological and land surface modeling and machine learning tools to advance the predictability of water resources across time and space. Results indicated that spatial downscaling of GRACE data to 1km resolution largely improved the groundwater storage estimations when compared with the groundwater monitoring data. All twenty sub-regions in the Indus Basin indicated irreversible decline in terrestrial water storage (TWS) and groundwater storage (GWS) between 2002 and 2023. However, the declining rate was dominant in the downstream areas where human activities are major influencing factors. By employing machine learning models, we reconstructed high-resolution (1km) data of groundwater level, enabling better monitoring and understanding of groundwater changes and facilitating more accurate groundwater management in these regions. By combining downscaled GRACE data with SWAT hydrological fluxes in spatial water balance, we also seek to improve groundwater depletion estimations in 55 canal command areas for understanding impacts of different cropping systems, growing food productions and unequal distributions of surface water. The significance of PhD dissertation research is amplified by the generation of high-resolution terrestrial water storage dynamics, precipitation, water table, and groundwater depletion datasets in data-sparse areas across transboundary Basin, which we made publicly available on platforms such as Figshare, enabling researchers and policymakers to access valuable information for informed decision-making. The methodologies and programming tools developed during this research are applicable globally, offering solutions to regions grappling with environmental challenges.