Around 2.6 billion people are at risk of tooth carries and fluorosis worldwide. Quetta is the worst affected district in Balochistan plateau. Endemic abnormal groundwater fluoride ( \({\text{F}}^{-}\) ) lacks spatiotemporal studies. This research integrates geospatial distribution, geochemical signatures, and data driven method for evaluating \({\text{F}}^{-}\) levels and population at risk. Groundwater \({\text{F}}^{-}\) ranged from 0 to 3.4 mg/l in (n = 100) with 52% samples found unfit for drinking. Through geospatial IDW tool hotspot areas affected with low and high groundwater \({\text{F}}^{-}\) levels were identified. Geochemical distribution in geological setups recognized sediment variation leads to high \({\text{F}}^{-}\) (NaHCO3) and low \({\text{F}}^{-}\) (CaHCO3) water types in low elevation (central plain) and high elevation (mountain foot) respectively. Results of the modified water quality index identified 60% samples to be unsuitable for drinking. Support vector machine (SVM), random forest regression (RFR) and classification and regression tree (CART) machine learning models found \({\text{Na}}^{+}\) , Salinity and \({\text{Ca}}^{+2}\) as important contributing variables in groundwater \({\text{F}}^{-}\) prediction. CART model with R2 value of 0.732 outperformed RFR and SVM in predicting \({\text{F}}^{-}\) . Noncarcinogenic health risk vulnerability from \({\text{F}}^{-}\) increased from Adults < Teens < Children < Infants. Infants and children with hazard quotient values of 11.3 and 4.2 were the most vulnerable population at risk for consuming \({\text{F}}^{-}\) contaminated groundwater. The research emphasizes on both nutritional need and hazardous effect of \({\text{F}}^{-}\) , and development of desirable limit for \({\text{F}}^{-}\) .
