Influenza A viruses (IAVs) have historically posed significant public health threats, causing severe pandemics. Viral host specificity is typically constrained by host barriers, limiting the range of species that can be infected. However, these barriers are not absolute, and occasionally, cross-species transmission occurs, leading to human outbreaks. Early identification of changes in IAV host specificity is, therefore, critical. Despite advancements, identifying host susceptibility from genomic sequences during outbreaks remains challenging. Timely predictions are critical for effective real-time outbreak management and risk mitigation during the early stages of an epidemic. To address this, we proposed Flu-level Convolutional Neural Networks (Flu-CNN), a model designed to analyze genomic segments and identify IAV host specificity, with a particular focus on avian influenza viruses that could potentially infect humans. Extensive evaluations on large-scale genomic datasets containing 911,098 sequences show that Flu-CNN achieves an impressive 99% accuracy in determining host specificity from a single genomic segment, even for high-risk subtypes like H5N1, H7N9, and H9N2, which have a limited number of viral strains. Given its high level of accuracy, the model was applied to identify key mutations and assess the zoonotic potential of these strains. Furthermore, our study presents a pioneering approach for predicting IAV host specificity, offering novel insights into the evolutionary trajectory of these viruses. The model’s significance extends beyond evolutionary analysis, playing a pivotal role in outbreak surveillance and contributing to efforts aimed at preventing the viral spread on a global scale.