<p>A fresh approach to examining how climatic circumstances affect changes in groundwater level is required because of the effects of climate change, which include increasing heat, more rain, and an increase in the frequency of extreme weather events like storms and floods. Predicting changes in farmland groundwater level (GWL) with accuracy and simplicity is a crucial component of agricultural management. The framework that has been proposed combines eight different meteorological variables with four different machine learning (ML) models: random forest (RF), support vector machine (SVM), artificial neural network (ANN), and stacking ensemble model. The effectiveness of the models was tested using the GWL changes and meteorological variables (MV) data of nine borehole locations (Adwumako, Anyinam, Pamen, Ayensu, Doboro, Nsawam, Pokrom, Suhum, and Old Tafo) in the Eastern region of Ghana from 1996 to 2006. According to the findings, the nine borehole datasets had marginally different model performances. The stacking ensemble model performed best, with root mean square error (RMSE) ranging from 0.0801 to 0.1123, mean absolute error (MAE) ranging from 0.0100 to 0.0376, mean absolute percentage error (MAPE) ranging from 0.0872 to 0.1250, and R-squared (R²) ranging from 0.9507 to 0.9819. Almost all ML models had R² values greater than 0.90 throughout the testing stage, indicating significant performance. The performance of the proposed model is compared with existing basic ML and stacking ensemble models which performed better in respect to RMSE, MAE, MAPE, and R². In addition, the findings indicate that the model is suitable for predicting GWL changes for sustainable water resource management, which will assist the government, agencies, and organizations in making decisions under changing climatic conditions. Finally, due to the close similarity of the year-round climate of neighboring West African countries, the model may also be used to predict changes in groundwater levels in those countries.</p>