Lake hydrological simulations using physically based models are cumbersome due to extensive data and computational requirements. Despite an abundance of previous modeling investigations, real-time simulation tools for large lake systems subjected to multiple stressors are lacking. The back-propagation neural network (BPNN) is applied as a first attempt to simulate the water-level variations of a large lake, exemplified by the Poyang Lake (China) case study. The BPNN investigation extends previous modeling efforts by considering the Yangtze River effect and evaluating the influence of the Yangtze River on the lake water levels. Results indicate that the effects of both the lake catchment and the Yangtze River are required to produce reasonable BPNN calibration statistics. Modeling results suggest that the Yangtze River plays a significant role in modifying the lake water-level changes. Comparison of BPNN models to a 2D hydrodynamic model (MIKE 21) shows that comparable accuracies can be obtained from both modeling approaches. This implies that the BPNN approach is well suited to long-term predictions of the water-level responses of Poyang Lake. The findings of this work demonstrate that BPNN can be used as a valuable and computationally efficient tool for future water resource planning and management of the Poyang Lake.