Integrative Machine Learning (ML) and optimization have risen to the challenges associated with disease prediction through improvement of accuracy, efficiency, and decision-making in health. This chapter focuses on the integration of the feature selection and hyperparameter tuning with predictive improvement of an ML algorithm through the help of some optimization techniques. While a literature review presents novel developments in the use of ML for the prognosis of chronic and infectious diseases and optimization techniques as genetic algorithms, PSO, SA. Additionally, the chapter analyzes the tendencies in the integrative approaches' effectiveness compared to the standard methods for knowledge discovery; how it may address big quantities of medical data; and how enhancing the interpretability of results is achieved. This work underlines the relevance and potential of integrative ML and optimization in improving the health care results. © 2025 Elsevier B.V., All rights reserved.