Metaheuristic Optimization Review MOR 3066-280X 10.54216/MOR https://www.americaspg.com/journals/show/3369 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 Christos Gatzoulis School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, Isa Town, Bahrain Osama Al Abedallat This paper explores the potential of machine learning (ML) in revolutionizing the screening and prognosis of dengue fever, a pervasive viral illness transmitted through the bite of infected mosquitoes prevalent in tropical and subtropical areas. Historically, traditional approaches to monitoring outbreaks have been hampered by a lack of precision and timing, creating an opportunity for machine learning to rectify datasets and uncover patterns that enhance accuracy. The paper introduces Random Forests, Support Vector Machines, Neural Networks, and combined classification models, along with their advantages, disadvantages, and the potential for incorporating external data such as climatic factors, population data, real-time Twitter data, etc. The results demonstrate significant increases in accuracy from the models, but it is clear that their applicability is contingent on localized approaches suitable for the regions. This underscores the importance of the quality and completeness of data used in the models. Current research indicates that data availability and the refinement of these models require a collective approach. The work underscores the potential of ML to redefine the paradigm of outbreak management in dengue and other vector-borne diseases, offering hope for improved public health worldwide. 2024 2024 12 21 10.54216/MOR.010202 https://www.americaspg.com/articleinfo/41/show/3369