Metaheuristic Optimization Review MOR 3066-280X 10.54216/MOR https://www.americaspg.com/journals/show/3405 2024 2024 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt Marwa Marwa School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, Isa Town, Bahrain Shomona Jacob The present review aims to describe the impact of machine learning techniques in health risk prediction, including the progress, drawbacks, and potential development. ML approaches in health care have become more effective in risk prediction than simple regression techniques because of their accuracy, scalability, and personalization. A Statistical tool like Decision trees, Support vector machines, and neural networks allows or examine non-linear genetic and environmental interactions with lifestyle factors. The review's main points are the increase in relevance of more complex types of models like ANN-PSO, a combination of two algorithms for feature selection, higher prediction accuracy, and other fields, including healthcare. These innovations have shown a unique success rate in identifying diseases, including obesity, diabetes, and any cardiovascular diseases, for better prevention measures and avenues of cure. Nevertheless, there are several difficulties: inferior quality of the data, the question of privacy, and explaining the decision-making of the modern complex models. Solving these issues calls for effective data governance, explainable artificial intelligence, and a multi-disciplinary approach to create and deliver transparency and fairness. As mentioned in the review, feature importance analysis like SHAP also carries plenty of significance for enhancing model interpretability to chase positive alterations. Concerning the outlook, implementing ML in the current HC system will require investment in data platforms, clinician expertise, and broader, suitable systems. As a result, new opportunities for using ML in connection with population health, patient and client outcomes, and receiving individualized care point out the further evolution of the transforming impact of technology. This paper offers an understanding of how health risk prediction and the public health strategy might benefit from new applications of ML and how the moral and practical issues of this new application of the technology may be dealt with. 2024 2024 01 13 10.54216/MOR.020101 https://www.americaspg.com/articleinfo/41/show/3405