Groundwater is a very important water resource at Kumamoto City. Kumamoto City is the capital city of Kumamoto Prefecture, which is located in the Kyushu region, Japan. All domestic water is obtained from groundwater in Kumamoto City. Modeling groundwater is a difficult issue. Conditions under the ground are complex, and difficult to be obtained. Even the delineation of a groundwater basin is frequently unknown. Nowadays, deep learning is a hot topic in many research fields including geoscience. A recurrent neural network (RNN) is a type of deep learning that is suitable for time series modeling. Then, it has been successfully applied for groundwater modeling. Therefore, this study utilized a new type of RNN, Long and Short-Term Memory (LSTM) network, to model groundwater level at a monitoring well within Kumamoto City. The results in this study showed good agreement with the observed groundwater. In addition, it is known that severe earthquakes in April 2016 affected the groundwater level around Kumamoto City. The groundwater level model by LSTM was also utilized to estimate the effects of the severe earthquakes on the groundwater level. The results indicated that the earthquakes may have increased the groundwater level at Kumamoto City by more than 3 m.