The research presents how Artificial Neural Networks can predict TDS, total alkalinity, and total hardness at Kunta in the River Sabari, a tributary of the River Godavari in India. River water quality models were developed using six measurable variables as inputs: total alkalinity, total hardness, calcium, magnesium, fluorides, carbonates, bicarbonates, and river flow. The correlation coefficient and mean square error were used to evaluate these models’ performance. The findings suggest that ANN-based river water quality models have the ability to simulate and forecast river water quality parameters because the projected values in the models were in close agreement with the observed values in the river water.