<p>The purpose of this study is to investigate the determinants of the medium-term trend of the CIP from 2000 to 2021 by solving a classification problem for a significant sample of 148 economies using supervised machine learning (ML) techniques. Demonstrating the generalizability of the features and algorithms grounded in the system approach of structural change (SC) facilitated the unveiling of the determinants that trigger the countries’ structural tendencies, thereby shaping the CIP midterm trend. Cumulative causation postulates underpin the role of the features in unleashing cumulative and feedback effects to delineate countries’ structural tendencies. The ML techniques employed are feature selection (FS), the validation set (VS) approach, the resampling approach, Shapley additive explanations (SHAP) value estimation, and several training algorithms. The training algorithms employed included logistic regression (LR), logistic regression with LASSO (LR_LASSO), linear discriminant analysis (LDA), decision tree (DT), extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) methods. The algorithms demonstrated significant performance in out-of-sample prediction; the VS metrics had accuracy values between 0.79 and 0.90, Youden index values between 0.6 and 0.78, and Cohen’s kappa values between 0.59 and 0.78. LDA, LR_LASSO, and ANN emerged as the most effective algorithms, but all exhibited remarkable generalizability. The SHAP value was critical both in assessing the feature importance and dependence for predicting the CIP midterm trend, revealing complex and nonlinear relationships that provide actionable insights with theoretical and policy implications for the SC field.</p>