Water is an essential resource that sustains life, supports economic activities, and maintains ecological balance. However, with rising global population, industrialization, urbanization, and climate variability, the demand for water continues to increase, putting immense pressure on its availability and management. This proposed work focuses on the multifaceted challenge of managing water resources effectively, ensuring both short-term demands and long-term sustainability. The primary aim of this study was to analyse water resource data comprehensively and develop predictive models to address critical aspects of water management. Using data science techniques, we examined factors influencing reservoir water levels, such as rainfall, evapotranspiration, domestic and industrial usage, and agricultural demands. A multi-faceted approach was adopted, combining time-series forecasting, machine learning, and optimization strategies to generate actionable insights and practical solutions. The study addresses this challenge by analyzing historical data to predict future water requirements using advanced statistical and machine learning models. It also evaluates reservoir storage capacities to determine their adequacy in meeting projected demands. By integrating forecasting with capacity assessment, this study provides actionable insights to enhance decision-making for sustainable water management.