Designing thin-walled plate structures is challenging due to their susceptibility to various forms of structural instability. In addition, the substantial computational cost of finite element analyses, especially in optimization scenarios, underscores the need for efficient and reliable surrogate models. To address this challenge, the present study employs machine learning (ML) techniques to predict the buckling response of thin plates under complex boundary conditions. Four ML models, including XGBoost, CatBoost, Light GBM, and Random Forest, are developed to predict the buckling coefficient based on input features, including aspect ratio, boundary condition, and compressive loading pattern. The training data for these models is generated using the finite integral transform method. Model performance is rigorously evaluated, with all four algorithms demonstrating strong predictive capabilities. Among them, XGBoost demonstrates the superior predictive performance, achieving an R² value of 0.99. To gain deeper insights into feature influence, SHAP analysis is conducted, revealing that the aspect ratio has the greatest influence on buckling coefficient, followed by boundary conditions and compressive loading. By adopting gradient-boosting approaches, the proposed framework demonstrates improved generalization and reduced overfitting, with potential applicability to structural optimization. The results suggest that integrating machine learning with structural analysis can serve as a computationally efficient approach for the design and optimization of thin-walled plates.