Accurate prediction of shale methane adsorption capacity is crucial for reservoir evaluation. This study integrates 486 experimental datasets to develop a multivariate machine learning prediction model. Six key geological parameters, including depth, total organic carbon (TOC), moisture, porosity, vitrinite reflectance (Ro), and clay minerals, were selected as features. Correlation analysis methods were used to reveal the nonlinear relationships between various geological parameters and methane adsorption capacity, as well as the intrinsic coupling structures among these parameters. The predictive performance of three machine learning models, namely, Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), was systematically compared. The results show that TOC and Ro are the significant factors controlling methane adsorption. The XGBoost model achieved superior performance on both training and test sets, demonstrating higher prediction accuracy and stronger generalization capability compared to SVM and RF. This study provides novel insights and methodologies for assessing shale gas adsorption potential and optimizing development strategies.