The integration of machine learning (ML) techniques in water resource engineering has emerged as a powerful approach to enhance the understanding and management of hydrological processes. This study investigates the role of ML in analyzing land use and land cover (LULC) changes and their impacts on hydrology in the urban context of Lucknow, India. The rapid urbanization in Lucknow has led to significant alterations in LULC, affecting hydrological cycles, including surface runoff, groundwater recharge, and water quality. The study explores various ML models, such as regression models, decision trees, and neural networks, applied to hydrological data. These models were assessed for their effectiveness in predicting key hydrological variables like precipitation, runoff, and evapotranspiration. The findings indicate that ML models, with their capacity to handle large datasets and identify complex patterns, significantly outperform traditional hydrological models in terms of accuracy and reliability. The implications of LULC changes on water resources are profound. Urbanization increases impervious surfaces, leading to higher runoff and reduced groundwater recharge, which exacerbates flooding and water scarcity issues. ML models provide robust tools for predicting these changes and enabling proactive management strategies. This work highlights the transformative potential of ML in water resource management, offering more precise and actionable insights. It suggests future research directions, including integrating real-time data for dynamic modeling and developing hybrid models that combine ML with traditional hydrological approaches. These advancements could foster more resilient and sustainable urban water management practices, which are essential for cities like Lucknow, which are facing rapid urban growth.