Traditional methods of pressure measurement of buildings are costly and time consuming. As an alternative to the traditional methods, this study developed a fast and computationally economical machine learning-based model to predict surface-averaged external pressure coefficients of a building with an unconventional configuration using three tree-based regressors: Adaboost, Extra Tree, and Random Forest. The accuracy and performance of the tree-based regressors were compared with a fourth-order polynomial function and a high-order non-linear regression proposed by an Artificial Neural Network (ANN). The comparison revealed random forest and extra tree models were simpler and more accurate than the polynomial functions and the ANN model. Alternatively, a machine learning interpretability method-Local Interpretable Model-agnostic Explanations (LIME) – was used to quantify the contribution of each parameter to the models' outcomes. LIME identified the most influential parameter, the variation in the influence of parameters with their values, and interactions of parameters. Moreover, LIME confirmed the tree-based regressors closely follow the flow physics in predicting external wind pressures rather than solely relied on training data.