This study examines the role of the Building Shape Coefficient in cooling load estimation by comparing statistical correlation and machine-learning feature importance. A dataset of 10,000 simulated building cases was generated using HAP, EnergyPlus, and jEPlus Parametric Study, incorporating seven building and thermal parameters, including floor level, window-to-wall ratio, gross floor area, target temperature, external wall U value, window U value, and the Building Shape Coefficient. After preprocessing and outlier removal, correlation analysis and machine learning with Support Vector Machine, Random Forest, and XGBoost were applied. The statistical results show that the Building Shape Coefficient has a moderate negative correlation with cooling load, confirming that compact buildings generally require less cooling. However, features such as floor level, gross floor area, and window-to-wall ratio demonstrated stronger positive correlations and greater influence. Machine learning analysis further revealed that the Building Shape Coefficient contributed less consistently across models. Its influence was weak in Support Vector Machine, moderate in Random Forest, and strongest in XGBoost when combined with thermostat setpoints. SHAP analysis confirmed that while the Building Shape Coefficient is physically significant, its predictive role is overshadowed by other dominant variables. The findings suggest that the Building Shape Coefficient remains an important geometric indicator of cooling demand, but design strategies should prioritize floor level, gross floor area, and window-to-wall ratio as primary drivers of cooling load. The integration of statistical correlation and machine learning interpretability provides a comprehensive framework for evaluating the role of building geometry in energy performance analysis.