AI is becoming increasingly recognised as a key enabler of Sustainable Development Goals 6 and 7, but there is a lack of integrated evidence of AI applications in the Water–Sanitation–Hygiene and Clean Energy (WASH–CE) nexus. The study presents bibliometric analysis of 11,817 publications (2016–2024) collected from Dimensions database. VOSviewer and Biblioshiny were used for bibliometric mapping and network analyses to assess publication trends, major journals and institutions, keywords, and international collaboration. Outcomes point to a pronounced upsurge in AI-infused WASH-CE (Water, Sanitation and Hygiene is an acronym for WASH) research post-2019, registering an average annual growth rate of 23.47%. China was the biggest contributor followed by the United States, India, and Australia, while Asian and Middle Eastern countries collaborated strongly with one another. This thematic evolution signifies a shift from traditional statistical methods to more advanced artificial intelligence techniques such as deep learning, transformer models, reinforcement learning, federated learning, explainable AI, etc. The paper proposes a systematic mapping of global research patterns which will help identify structural gaps and emerging priorities and thereby act as a foundation for evidence-based, scalable, inclusive and policy-relevant WASH–CE AI solutions.
Gupta et al. (2026) studied this question.