Background: Influenza viruses are etiological agents which cause contagious respiratory, seasonal epidemics and, for Influenza A subtypes, pandemics. The clinical picture of Influenza has undergone continuous change over the years, due to intrinsic viral evolution as well as ‘reassortment’ of its genomic segments. The history of Influenza highlights its ability to adapt and to rapidly evolve, without specific circumstances. This reflects the complexity of this pathology and poses the fundamental question about its assumption as a ‘common illness’ and its impact on public health. Summary: The global influenza epidemics and pandemics claimed millions of deaths, leaving an indelible mark on public health, and showing the need for a better comprehension of the influenza virus. The clear understanding of genetic variations during the Influenza seasonal epidemics is a crucial point for developing effective strategies for prevention, treatment, and vaccine design. The recent advance in Next Generation Sequencing approaches, model systems to virus culture and bioinformatics pipeline played a key role in the rapid characterization of circulating Influenza strains. In particular, the increase of computational power allowed to perform complex tasks in healthcare setting through Machine Learning (ML) algorithms, which analyze different variables, such as medical and laboratory outputs, to optimize medical research and to improve public health systems. The early detection of emerging and re-emerging pathogens is of matter importance to prevent next pandemics. Key Messages: The perception of influenza as a ‘trivial flu’ or a more serious public health concern is a subject of ongoing debate, reflecting the multifaceted nature of this infectious disease. The variability in the severity of influenza shed the light on the unpredictability of the viral characteristics, coupled with the challenges in accurately predicting circulating strains. This adds complexity to the public health burden of Influenza and highlights the need of targeted interventions.