Due to nonuniform rainfall distribution in Taiwan, groundwater is an important water source in certain areas that lack water storage facilities during periods of drought. Therefore, groundwater recharge is an important issue for sustainable water resources management. The mountainous areas and the alluvial fan areas of the Jhuoshui River basin in Central Taiwan are considered abundant groundwater recharge regions. This study aims to investigate the interactive mechanisms between surface water and groundwater through statistical techniques and estimate groundwater level variations by a combination of artificial intelligence techniques and the Gamma test (GT). The Jhuoshui River basin in Central Taiwan is selected as the study area. The results demonstrate that: (1) More days of accumulated rainfall data are required to affect variable groundwater levels in low-permeability wells or deep wells; (2) effective rainfall thresholds can be properly identified by lower bound screening of accumulated rainfall; (3) daily groundwater level variation can be estimated effectively by artificial neural networks (ANNs); and (4) it is difficult to build efficient models for low-permeability wells, and the accuracy and stability of models is worse in the proximal-fan areas than in the mountainous areas.