Abstract: The buffeting of supercritical airfoils significantly impacts the safety and stability of transport aircraft. Efficient and accurate determination of buffeting boundaries has been a focal point of research. In this study, a prediction framework for buffeting boundaries of supercritical airfoils was developed using Long Short-Term Memory (LSTM) neural networks, focusing on the CHN-T1 transport aircraft model. Utilizing computational data from the CHN-T1 model, LSTM-based models for predicting aerodynamic coefficients and determining buffeting onset angles were designed. These models forecast changes in aerodynamic coefficients accurately at a given Mach numbers and rapidly determine buffeting onset angles at a given Mach numbers. Integration of buffeting onset angle data ultimately defined the buffeting boundaries of the CHN-T1 model, validated with wind tunnel experimental data. The results demonstrated the LSTM model's excellent predictive capabilities for aerodynamic coefficient trends, maintaining a RMSE within 2%. Furthermore, the model exhibited outstanding performance in buffeting onset angle determination, with errors remaining within 2%. These findings validate the reliability and accuracy of this approach in buffeting boundary prediction, providing robust support for research on supercritical airfoil buffeting.