Artificial Intelligence for Sustainable Water Management: Opportunities, Technologies, and Environmental Trade-offs

Water scarcity is one of the major resource challenges of the twenty-first century. Agriculture alone accounts for roughly 70 percent of global freshwater withdrawals, and a significant proportion of that water is lost before reaching plant roots through evaporation, runoff, and inefficient delivery. Urban distribution networks add to the problem: treated water leaks silently through ageing pipes, never reaching consumers. With global food production needing to increase by 70 percent by 2050 to feed a projected population of 9.1 billion, the strain on already-limited freshwater reserves will intensify unless efficiency improves substantially. This review examines how artificial intelligence (AI), the Internet of Things (IoT), unmanned aerial vehicles (UAVs), and digital-twin modelling are being applied across two domains: precision agriculture and urban water infrastructure. In agriculture, AI-driven irrigation, soil-moisture sensing, and UAV-based crop monitoring have produced documented water savings of 30 to 70 percent alongside yield improvements of 20 to 30 percent. In cities, smart metering and AI-based leak detection have measurably reduced water losses in deployed systems across Spain, the United Kingdom, and the United States. The review also examines a less-discussed counterpart: AI's own water cost, specifically the freshwater consumed for data-centre cooling during large-model training and inference. The paper argues that AI's net contribution to water sustainability depends on deliberate choices, including model efficiency, renewable-powered data centres, and transparent water-use reporting, working alongside sound policy and infrastructure support.