Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/3353 2022 2022 Department of Chemistry and Biochemistry, University of Arkansas, Fayetteville, AR 72701, USA Ehsaneh Ehsaneh In this review paper, the authors discuss the development and application of methods for modeling and control and comparison of viral spreading in society with fractional-order and ML techniques for data analysis. Some of the most well-known epidemiological models are based on traditional approaches to describing disease diffusion and often need to be more sufficient when mapping the realistic disease distribution. However, fractional-order models give more flexibility and accuracy due to the memory incorporated and interaction factors. Moreover, the amalgamation of ML and artificial intelligence allows the analysis of considerable and heterogeneous amounts of data, enabling real-time prediction and favorable outbreak response measures. This paper outlines some benefits of integrating these sophisticated techniques while discussing issues such as the quality of inputs, problems in the methods deployed, and issues of visibility of the methods deployed. Finally, it proposes better epidemic preparedness and response through interdisciplinary approaches that emphasize the role of these technologies in a society that is more vulnerable to epidemic diseases. 2024 2024 37 45 10.54216/JAIM.080205 https://www.americaspg.com/articleinfo/28/show/3353