• Divyanka Kaloti
• Ishan Bajaj
• Mayank Patel

Safe drinking water is essential for public health, yet contamination from natural and anthropogenic sources remains a major threat worldwide. This research proposes an end-to-end pipeline using machine learning (ML) to evaluate water quality from multiple geological regions, identify contamination risks, predict associated health hazards, and recommend region-specific treatment methods. The study combines field sampling, laboratory analysis, geospatial data, healthcare data integration, and ML modeling to build an intelligent early-warning system for water safety. The outcome is aimed at helping environmental agencies and healthcare systems make evidence-based, location-specific interventions.

Machine Learning, Water Quality Index, Public Health Risk, Random Forest, XGBoost, SVM, Radiological Contamination, Multiregional Analysis

"Health Risk Analysis Using Water Quality Analysis Through Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.205-209, June-2025, Available :http://www.jetir.org/papers/JETIRGW06032.pdf

"Health Risk Analysis Using Water Quality Analysis Through Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. pp205-209, June-2025, Available at : http://www.jetir.org/papers/JETIRGW06032.pdf