Title: AROGYAJAL: AI and IoT-Enabled Framework for Water Safety and Disease Prevention


Authors: Priyanshu Sharma, Kshitij Chitranshi, Lovekush Nagda, Ishan Raut, Shubh Sharma, Sweta Gupta


Published in: Volume 3 Issue 1 Jan June 2026, Page No. 21-27


DOI: 10.63844/IJAITR.v3.i1.2026.21-27 cite


Keywords: IoT, Water Quality, Disease Detection, Health Surveillance, Rural Healthcare.


Abstract: This project introduces a low-cost, easy-to-scale Smart Health Surveillance and Early Warning System aimed at preventing water-borne diseases in rural and tribal regions. It uses inexpensive sensors to check important water quality factors like pH, turbidity, and temperature. Local health workers use a simple, multilingual mobile app to share health information. By matching sensor data with health reports, the system can spot early signs of disease outbreaks such as diarrhea, cholera, and typhoid. The system allows data to be collected even without the internet and automatically uploads it when connectivity is available. Automatic alerts are sent to health workers and community leaders so they can act quickly. By combining sensor monitoring, community involvement, and easy-to-use technology, the system boosts awareness, improves public health actions, and lowers the eff ect of diseases. Experimental results show that the system detects water-borne illnesses early, speeds up responses, and enhances health care in remote areas, proving its eff ectiveness in safeguarding at-risk communities.


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