India places a high value on groundwater, essential to supplying the country’s water needs. There is a great deal of concern regarding the depletion of groundwater sources due to the growing demand for it. Water preservation and drought mitigation depend on the ability to forecast groundwater supplies. The water will be utilized based on its availability. Water scarcity is a result of both climate change and population increase. The need for groundwater would have grown as a result. However, the distribution of groundwater is not uniform. Tools for machine learning may enhance the forecasting of groundwater. The modeling’s input variables depend on how much water is utilized and recharged over different seasons. Our suggested approach forecasts the yearly groundwater availability for use in the future to anticipate the average availability of groundwater and usage of extraction for the year 2025 using machine learning algorithms including logistic regression, random forest, support vector machine, and KNN. To have a better grasp of the water we have to benefit from it all, a reliable groundwater level forecast is necessary. It has been discovered that each of the models can produce accurate forecasts, having the technique of random forest outperforming the others with an 82% accuracy rate.