Lake water level is an essential indicator of environmental changes caused by natural and human factors. The water level of Poyang Lake, the largest freshwater lake in China, has exhibited a dramatic variation for the past few years, especially after the completion of the Three Gorges Dam (TGD). However, there is a lack of more accurate assessment of the effect of the TGD on the Poyang Lake water level (PLWL) at finer temporal scales (e.g., the daily scale). Here, we used three machine learning models, namely, an Artificial Neural Network (ANN), a Nonlinear Autoregressive model with eXogenous input (NARX), and a Gated Recurrent Unit (GRU), to simulate the daily lake level during 2003–2016. We found that machine learning models with historical memory (i.e., the GRU model) are more suitable for simulating the PLWL under the influence of the TGD. The GRU-based results show that the lake level is significantly affected by the TGD regulation in the different operation stages and in different periods. Although the TGD has had a slight but not very significant impact on the yearly decline of the PLWL, the blocking or releasing of water at the TGD at certain moments has caused large changes in the lake level. This machine-learning-based study sheds light on the interactions between Poyang Lake and the Yangtze River regulated by the TGD.