Groundwater serves as a valuable resource to supplement surface water, and its extensive utilization underscores the importance of precise groundwater level predictions. Burkina Faso confronts a critical challenge in the domain of sustainable groundwater resource management, underscoring the need for accurate forecasts of groundwater levels to enable efficient resource allocation and ensure long-term sustainability. This study introduces a robust framework that uses state-of-the-art Artificial Intelligence methodologies to predict groundwater levels across six strategically located piezometers in Burkina Faso’s Central Plateau region. The dataset combines piezometric Measurements, Rainfall, and vegetation indices that serve as a multifaceted feature space for model training. We systematically evaluated the performance of three specific machine learning models-NeuralProphet, XGBoost, and Long Short-Term …