aspg-41-4420-1792223359 20261017074919 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Metaheuristic Optimization Review MOR 3066-280X 2026 7 1 Arian Rabet Department of Integrative Biology, College of Letters and Science, University of California, Berkeley, Berkeley, CA, USA Ehsan Khodadadi Department of Chemistry and Biochemistry, University of Arkansas, Fayetteville, AR, 72701, USA Machine learning and optimization techniques have significantly changed the healthcare industry, especially in finding and managing essential and dangerous diseases like lung cancer, breast cancer, diabetes as well as heart disease. Lung cancer, which is among the common fatal cancers, requires proper subtyping before proper management is made. This has been achieved through machine learning alongside radiomics, where detailed imaging characteristics of the tumor from CT scans are retrieved without invasive procedures. In the same way, machine learning has provided much higher detection, diagnosis and treatment levels of breast cancer, diabetes and heart disease. This literature review sums up the priorities of studies showing the benefits of using machine learning and bio-inspired optimization methods to address the challenges posed by disease classification and prediction. Such complications have proved great potential in improving the diagnostic methods used for early intervention and, thereby, accurate and efficient diagnosis of a problem, developing an appropriate treatment plan and, thus, improving the patient caring methods and scenario, which has played an imperative role determining the future of modern-day health care. 2026 18 28 4420 https://creativecommons.org/licenses/by/4.0/ 10.54216/MOR.070103 https://americaspg.com/journal/41/article/4420 https://americaspg.com/storage/articles/manuscripts/01M336SSZ0J2FJ1TGT8193ERCH.pdf