aspg-28-3523-1792148504 20261016110144 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2025 9 1 Marwa M. Eid Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt Anis Ben Ghorbal Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia A significant barrier to sustaining and achieving successful quality evaluation of apples has been the biases and inefficiencies of manual inspection, which cannot be effectively applied to large-scale industrial processing. This paper fills this gap by building an automated machine learning system for apple quality classification, which combines physical (size, weight, firmness) and sensory (sweetness, acidity, juiciness) features. Four baseline models, including K-Nearest Neighbors (KNN), Naive Bayes, Decision Tree, and Gradient Boosting, were trained on a preprocessed dataset. Imputation, categorical encoding, and feature scaling were used. To enhance predictive accuracy, hyperparameter tuning of three metaheuristic optimization algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Greylag Goose Optimization (GGO)—was employed. The findings indicated that KNN had the highest baseline accuracy of 93.6% and that its accuracy improved to 98.6% with GGO optimization, which was better than that of GA and PSO. These results indicate that GGO-optimized KNN can provide a high-quality and scalable approach to assessing the quality of apples in the industry, offering enhanced accuracy, reduced variability, and a high degree of practical applicability in supply chain decision-making, thereby increasing customer satisfaction and decreasing losses after harvesting. 2025 37 52 3523 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.090106 https://www.americaspg.com/journal/28/article/3523 https://www.americaspg.com/storage/articles/manuscripts/01M4Y41G8QNHVRM9KEPKN7X5R0.pdf