Groundwater is an essential resource in Jharkhand, a state in India that is home to millions of people who rely almost exclusively on groundwater for different uses. It is, therefore, vital and extremely important to study groundwater, and for groundwater to be monitored as it is quickly facing threats from anthropogenic sources such as industrial wastewater and agricultural leachate. This study assessed and projected groundwater quality in the study area by both conventional and advanced methodologies. Entropy Water Quality Index (EWQI) and Arithmetic Mean-WQI (AM-WQI) environmental indices were employed using nine hydrochemical parameters for determining drinking water quality suitability. Present study also assessed two machine learning algorithms, support vector machine (SVM) and extreme gradient boosting (XGBoost), using the same input data to develop models that predicted water quality indices. Model evaluation conditions demonstrated excellent levels of prediction achievement with SVM = 0.99 and XGBoost = 0.96. Spatial distribution map showed that the northern section of Jharkhand has the lowest groundwater quality, which appears to be related to high amounts of agriculture, irrigation return flow, and wastewater. Piper trilinear plots further showed that Ca–Mg–HCO3 dominate as hydrochemical facies relative to what would potentially contaminate the source area. Nonetheless, most groundwater was assessed as reasonably good to moderate levels of water quality with only localized water quality decline. This study demonstrates that water quality analysis, GIS mapping, and machine learning will be a highly useful approach to rapidly monitor and manage groundwater resources, which can be utilized to help manage sustainable water resources in similar semiarid environments.