Waterborne illnesses are a significant concern worldwide. The management of water resources can be facilitated by artificial intelligence (AI) with the help of data analytics, regression models, and algorithms. Achieving the Sustainable Development Goals (SDGs) of the 2030 Agenda for Sustainable Development of the United Nations depends on understanding, communicating, and measuring the value of water and incorporating it into decision-making. Various barriers are used from the source to the consumer to prevent microbiological contamination of drinking water sources or reduce contamination to levels safe for human health. Infrastructure development and capacity-building policies should be integrated with guidelines on applying AI to problems relating to water to ensure good development outcomes. Communities can live healthily with such technology if they can provide clean, economical, and sustainable water to the ecosystem as a whole. Quick and accurate identification of waterborne pathogens in drinking and recreational water sources is essential for treating and controlling the spread of water-related diseases, especially in resource-constrained situations. To ensure successful development outcomes, policies on infrastructure development and capacity building should be combined with those on applying AI to water-related problems. The primary focus of this study is the use of AI in managing drinking water and preventing waterborne illness.
Keywords: artificial intelligence; machine learning; safe water; water management; waterborne diseases.
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