aspg-41-4502-1792223338 20261017074858 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Metaheuristic Optimization Review MOR 3066-280X 2026 7 2 Ancy Cheriyan School of Artificial Intelligence, Bahrain Polytechnic ,PO Box 33349, Isa Town, Bahrain Diabetes prediction has become an important direction in clinical artificial intelligence because early identification of high-risk individuals can support preventive intervention, personalized monitoring, and improved long-term healthcare outcomes. This review examines recent machine learning, deep learning, and optimizer-assisted methodologies for diabetes prediction and related diagnostic decision support. It discusses how clinical variables, physiological measurements, biomedical signals, retinal images, population-specific attributes, and healthcare monitoring data can be transformed into predictive evidence through preprocessing, feature engineering, feature selection, model training, hyperparameter optimization, and risk interpretation. The review also emphasizes major methodological concerns, including class imbalance, model robustness, interpretability, subgroup variation, complication-oriented prediction, and deployment within connected healthcare environments. Overall, the reviewed literature indicates that effective diabetes prediction should be developed as an integrated clinical decision-support pipeline rather than as a single classifier comparison, with careful attention to data quality, feature relevance, model transparency, and practical healthcare usability. 2026 P. 01 12 4502 https://creativecommons.org/licenses/by/4.0/ 10.54216/MOR.070201 https://americaspg.com/journal/41/article/4502 https://americaspg.com/storage/articles/manuscripts/01M2K9BY4JNBGWVT25PNTYEW99.pdf