The problem revolves around the significant computational requirements hindering research on solar ponds despite their potential as an energy storage solution. Dynamic temperature fluctuations in different zones necessitate precise timing of energy extraction, demanding accurate long-term temperature forecasts. The study aims to employ machine learning to expedite temperature prediction and enhance the utility of solar ponds. This study uses meteorological data for three years and the Random Forest Regressor Algorithm to predict the temperatures of a solar pond’s Lower Convective Zone (LCZ) and Upper Convective Zone (UCZ). The input includes 10 parameters, and the output values represent the temperatures for 3 years. The dataset is generated using MATLAB for six locations and then merged to form the final dataset. The Random Forest Regressor Algorithm is trained and tested on this dataset, which gives an R-squared value of 0.998 and a Mean Absolute Percentage Error (MAPE) of 1.189%. The predicted data are validated against values from the MATLAB model, and the AI model is almost 1000 times faster than the MATLAB model. This study provides a reliable and efficient method for char acterizing a solar pond.