Authors : Bealo Deebari Brownson; Ayakpo Akpi; Bakpo Dumka John; Bealo Lekie Meedubari

Volume/Issue : Volume 11 - 2026, Issue 7 - July

Google Scholar : https://tinyurl.com/53un3yh8

DOI : https://doi.org/10.38124/ijisrt/26jul1337

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Water, sanitation and hygiene (WASH) remain critical public health and development challenges in SubSaharan Africa (SSA), where approximately 440,000 deaths from diarrheal diseases were recorded in 2024 alone, primarily due to water contamination by fecal pathogens. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools with the potential to address long-standing WASH challenges across the region. This review provides a comprehensive synthesis of the current state of AI applications in WASH across SSA, examining five key domains: water quality monitoring and prediction, sanitation infrastructure assessment, waterborne disease surveillance, citizen science and community engagement, and decision support for water resource management. Drawing upon peer-reviewed literature published between 2020 and 2026, alongside contemporary case studies from across the region, the review identifies significant opportunities including improved predictive accuracy for contamination events, scalable monitoring through remote sensing, and enhanced community participation through AI-powered tools while also highlighting persistent challenges, including data scarcity, infrastructural deficits, limited technical capacity, and ethical concerns around algorithmic governance. The analysis reveals that while AI-driven solutions have demonstrated substantial promise in research settings, translation to widespread operational deployment remains constrained by systemic barriers. We conclude by proposing a framework for responsible AI integration in WASH and identifying priority research directions for advancing resilient, equitable, and context-appropriate WASH services across Sub-Saharan Africa.

Keywords : Artificial Intelligence; Machine Learning; Water Quality; Sanitation; Hygiene; Sub-Saharan Africa; WASH; Remote Sensing; Citizen Science; Predictive Modeling.

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