aspg-28-3149-1793997378 20261106203618 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2024 7 2 Abdelhameed Ibrahim Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt Abdelaziz A. Abdelhamid Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt Ehab M. Almetwally Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia Education is central to social and economic development, making school enrollment and the quality of learning important global priorities. This study applies machine-learning regression to the World Educational Data dataset from Kaggle. After data exploration and preprocessing, eleven models are compared: Support Vector Regression (SVR), CatBoost, Random Forest, Extra Trees, XGBoost, a multilayer perceptron (MLP), Gradient Boosting, Decision Tree, K-Nearest Neighbors, Linear Regression, and a pipeline model. Performance is evaluated using MSE, RMSE, MAE, MBE, the correlation coefficient, R2, NSE, and WI. CatBoost reports the strongest correlation and coefficient of determination (both approximately 0.99999) with an MSE of 0.04024. The results illustrate the potential of machine-learning methods to support analysis and prediction of educational outcomes and to inform educational planning. 2024 38 42 3149 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.070206 https://americaspg.com/journal/28/article/3149 https://americaspg.com/storage/articles/manuscripts/01M5J4SQ1K5GVRMWX3SEKYA4TF.pdf