Impact of Artificial Intelligence on the Sustainable Use of Water Resources

This bibliometric study examines artificial intelligence’s impact on sustainable water management through systematic analysis of 424 publications from Scopus, Web of Science, and IEEE Xplore following the 2020 PRISMA guidelines. Four analytical approaches were implemented: descriptive bibliometric characterization, VOSviewer network visualization, principal component analysis with Ward’s hierarchical clustering (86.58% variance explained, cophenetic correlation = 0.951), and qualitative synthesis. The results reveal exponential growth from 4 publications (2018) to 167 (2025) with geographic concentration in China (30.2%), the USA (9.7%), and India (8.0%). Collaboration networks exhibit pronounced fragmentation (density = 0.04, modularity = 0.78) with minimal North–South partnerships (12%). Critically, keyword analysis identifies five thematic clusters dominated by machine learning methodologies, whereas governance and equity dimensions appear fewer than eight times, revealing a fundamental gap wherein technical optimization proceeds without the institutional frameworks necessary for equitable water access. Multivariate analysis suggests that technological infrastructure capacity is a stronger correlate of research output than geographic water stress, based on the observed geographic distribution of high-output nations rather than direct operationalization of scarcity indicators. The qualitative synthesis revealed that 68% of the studies remained pilot-scale studies, 82% were concentrated in developed nations, and 66% cited data quality as the primary constraint. The bibliometric patterns suggest a pronounced orientation toward computational approaches, alongside paradoxical AI infrastructure water consumption that may partially offset conservation benefits. (Note: 2025 figures reflect early-access articles retrieved before the November 2024 search date and should be interpreted as partial-year estimates.) Achieving sustainable water management requires a reorientation emphasizing measurement infrastructure in data-poor contexts, North–South partnerships, and the integration of socioinstitutional dimensions as constitutive elements within technical development frameworks.