Reservoirs play a critical role in global water resource management systems; however, reservoir operation simulation faces significant challenges under different hydrological conditions. This study compares the performance of four machine learning models (Random Forest, Gradient Boosting, Long Short-Term Memory, and Bidirectional LSTM) in reservoir outflow simulation under varying hydrological conditions for the Lianghekou reservoir in the Yalong River Basin, China. Results showed that Random Forest achieved the best overall performance (R2 = 0.6940), followed by Gradient Boosting (R2 = 0.6794), BiLSTM (R2 = 0.6588), and LSTM (R2 = 0.6247). Model performance exhibited clear year-type dependency, with accuracy generally declining from wet to dry years while maintaining consistent ranking. Dry years presented the most significant simulation challenges, with all models showing reduced accuracy. This study demonstrates that model selection should be tailored to specific application scenarios and hydrological conditions, providing valuable insights for intelligent water resource management systems and reservoir operation optimization.