The prediction of groundwater level is a viable strategy for attaining sustainable water resource management. Recently, machine learning techniques have gained popularity as an alternative to numerical and statistical time series models grounded in physical principles. These methods excel at spotting complex trends and non-linear relationships. This study applies and compares seven machine learning techniques to predict the 20-year monthly groundwater level over the Mashhad plain aquifer in Iran. A novel idea based on the Thiessen polygon is proposed to provide a compressive dataset for presenting groundwater level of whole aquifer where the machine learning models can be trained and tested. This is because there are several piezometric wells, rainfall gauges, and hydrometric stations in the entire aquifer. Extensive simulations and modelling are conducted to select the appropriate input combinations for each technique, to identify the best model, and to carry out sensitivity analysis using a novel criterion known as the Global Performance Index, which integrates several regression performance criteria. The results show that the well-known Long Short-Term Memory performs significantly better than its competitors. Its superiority is about 13% over the next technique. To have a more accurate Long Short-Term Memory model, the sensitivity analysis was performed to reach the optimal parameters are as 120, 0.17, and 100 for the number of neurons, dropout rate, and batch size, respectively. Furthermore, an uncertainty analysis using Moving Block Bootstrap is performed to ensure that all uncertain effects are eliminated.

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