A comprehensive analysis was conducted to examine the processes and impacts of water level variations in Qinghai Lake under changing climatic conditions. The study utilized monthly mean water level data from 1959 to 2017，sourced from the Qinghai Lake Basin，in conjunction with meteorological and climate variables derived from the ERA5 reanalysis dataset developed by the European Centre for Medium-Range Weather Fore‐ casts（ECMWF）. Several large-scale atmospheric circulation indices were also incorporated to investigate their influence on lake dynamics. This integrated dataset enabled a systematic assessment of the dominant climatic drivers and facilitated the development of predictive models to simulate future water level changes. To identify the most relevant influencing factors，the Random Forest（RF）algorithm was employed to perform feature selection and importance ranking. This process allowed for an evaluation of the relationship between feature relevance and model performance. Subsequently，a comparative analysis was undertaken using five machine learning models：RF，Support Vector Machine（SVM），Multi-layer Perceptron（MLP），Long Short-Term Memory（LSTM） networks，and Multiple Linear Regression（MLR）. The models were trained and validated to simulate monthly water level fluctuations and to assess the influence of model complexity and temporal learning ability on predictive accuracy. The analysis revealed that key drivers of Qinghai Lake water levels include the North Atlantic Oscillation（NAO），Atlantic Multidecadal Oscillation（AMO），Niño 3. 4 index，relative humidity at 400 hPa，450 hPa，and 100 hPa（RH400，RH450，RH100），precipitation，temperature at 1000 hPa（T1000），vertical wind velocity at 1000 hPa（W1000），and longwave radiation（LW）. Among the models tested，the LSTM network exhibited superior performance due to its ability to capture complex nonlinear and sequential dependencies in the data. When the ten most significant features were selected，the LSTM model achieved a Pearson correlation coefficient（R）of 0. 95，Nash-Sutcliffe Efficiency（NSE）of 0. 96，Normalized Root Mean Square Error（NRMSE） of 0. 14，and Kling-Gupta Efficiency（KGE）of 0. 87. The MLP model demonstrated the second-best performance，while RF and SVM yielded comparable but slightly lower results. MLR performed the worst，reflecting its limitations in modeling nonlinear and temporal relationships. Projections based on the LSTM model indicate that the water level of Qinghai Lake is likely to rise by approximately 2. 55 m between 2017 and 2030. This anticipated increase reflects the continuing influence of climate change and underscores the importance of adaptive water resource management strategies in plateau lake regions. The findings offer a reliable methodological frame‐ work for modeling and forecasting hydrological changes in alpine lake systems under future climate scenarios.