The H1N1 virus, known for its global impact on public health, continues to pose a significant threat, underscoring the need for effective antiviral strategies. Neuraminidase inhibitors have proven to be pivotal in mitigating the severity and spread of influenza viruses. In this study, we employ Quantitative Structure-Activity Relationship (QSAR) modeling to elucidate the molecular features influencing the inhibitory activity of compounds targeting the neuraminidase enzyme of the H1N1 virus.Molecular descriptors encompassing structural, physicochemical, and electronic properties are systematically selected to capture the essential features dictating the inhibitory potential of neuraminidase inhibitors.The mathematical models, whether linear or nonlinear, are developed through rigorous statistical analyses, emphasizing the interpretability of the relationships between molecular descriptors and biological activity. The acceptability domain is defined to ensure the reliability of predictions for structurally diverse compounds.The results showcase the robustness of the QSAR models in predicting the neuraminidase inhibitory activity of new compounds. Critical structural insights are uncovered, guiding the rational design of novel H1N1 virus neuraminidase inhibitors with enhanced efficacy and selectivity. The implications of these findings extend to the development of potential antiviral agents and contribute to the ongoing efforts to combat influenza outbreaks.Keywords: H1N1 virus, Neuraminidase inhibitors, Quantitative Structure-Activity Relationship (QSAR), Influenza, Antiviral drug design, Molecular descriptors, Biological activity.