aspg-27-4461-1792223527 20261017075207 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Artificial Intelligence and Education Technology IJAIET 2835-2432 2026 5 2 Hiteshkumar Solanki Scientist-D (CS), Information and Library Network (INFLIBNET) Centre, Gandhinagar, India Paresh Virparia Professor, P G Department of Computer Science & Technology, Sardar Patel University, Vallabh Vidyanagar, India Devika Madalli Director, Information and Library Network (INFLIBNET) Centre, Gandhinagar, India The paper presents a novel approach for predicting the efficiency of engineering higher educational institutions in India. The prediction model uses data envelopment analysis to solve linear programming problems and support vector regression as a supervised machine learning algorithm. DEA is a nonparametric tool for computing the relative efficiency of HEIs across multiple inputs and outputs. A total of 25 featured variables is considered, out of which 15 are input-oriented, whereas 10 are output-oriented. Input-oriented features are normalised using the standard z-score method, whereas output-oriented features are normalised using the Min–Max normalised method. A sample of 7432 engineering institutions is considered for the featured dataset. The featured dataset is split at a 90:10 ratio for training and testing. The hybrid model, combining traditional DEA with a supervised machine learning SVR, is trained on a training dataset. The prediction model is tested for estimating 743 engineering institutions. The model provides higher accuracy, along with a precision score, in the confusion matrix comparing the actual and predicted HEI performance categories. The best-fitting hybrid model also yields a predicted efficiency index for engineering HEIs, along with MAE, MSE, and RMSE, which are 0.0703, 0.0080, and 0.0894, respectively. The R-squared score of the prediction model is 0.7642. The developed predictive model can be generalised across sectors such as agriculture, banking, industry, and education. 2026 48 55 4461 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJAIET.050206 https://americaspg.com/journal/27/article/4461 https://americaspg.com/storage/articles/manuscripts/01M32B4RNS0MHN8KFP685PCYVR.pdf