Agricultural systems across the world face increasing stress from water shortages, climate change, and growing food demand. Traditional tools for tracking water use often lack the detail needed to support sustainable water management. This study explores how combining satellite-based remote sensing, virtual water footprint (VWF) analysis, and artificial intelligence (AI) can improve understanding of agricultural water use. It focuses on measuring green, blue, and grey water—representing rainfall, irrigation, and water needed to handle pollution. Satellite systems are used to estimate key variables such as evapotranspiration and soil moisture. AI methods, including neural networks and random forests, help increase accuracy and predict water use in areas where data is limited or conditions vary widely, such as semi-arid regions like Canada’s Prairie lands. These tools offer powerful new ways to monitor water use in real time, improve irrigation planning, and inform policy decisions. However, challenges remain, including dealing with uncertainty in models, limited tracking of grey water, and difficulty scaling these technologies for small farms. The study outlines ways to integrate these advances into water management strategies that support both food security and environmental sustainability.