Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/3352 2022 2022 Department of Chemistry and Biochemistry, University of Arkansas, Fayetteville, AR 72701, USA Ehsan Ehsan Influenza is often associated with millions of cases and hundreds of thousands of deaths each year, thus constituting a serious threat to public health. Traditional surveillance techniques employed in epidemiology are limited in forecasting impending outbreaks as caused by delays in receiving the relevant information and the dynamic nature of political environments. This review focuses on the available literature on the use of machine learning (ML) techniques in understanding and controlling influenza with an accent on all the sources of information available, including clinical papers, social networking sites and others. Applicable practices in classifying predictive modeling techniques, including deep learning and others, ensemble techniques, time series analysis, etc., have increased the speed and precision of the earlier results. Even so, the achievements made so far have not come on a silver platter as there are challenges, but not limited to data issues, model explain ability and strict validation processes. Some research areas are enhancing the present models to accommodate diverse virulent strains of the viruses and advancing extensive data analysis methods. It is noted in this review that machine learning strategies are essential in combating health issues and, thus, why such technologies can be deployed within a concise duration in the context of influenza epidemics for effective forecasting and resource management to salvage lives. 2024 2024 27 36 10.54216/JAIM.080204 https://www.americaspg.com/articleinfo/28/show/3352