The detection of evolutionary transitions in influenza A (H3N2) viruses' antigenicity is a major obstacle to effective vaccine design and development. In this study, we describe NIAViD, an unsupervised machine learning tool, adept at identifying these transitions, using HA1 sequence and associated physicochemical properties. NIAViD, performed with 88.9% (95% CI, 56.5%–98.0%) and 72.7% (95% CI,43.4%– 90.3%) sensitivity in training and validation respectively, outperforming the uncalibrated null model – 33.3% (95% CI,12.1%–64.6%) and does not require the need for potentially biased, time-consuming and costly laboratory assays. The pivotal role of Boman’s index, indicative of the virus’s cell surface binding potential, is underscored, enhancing the precision of detecting antigenic transitions. NIAViD's efficacy is not only in identifying influenza isolates that belong to novel antigenic clusters, but also in pinpointing potential sites driving significant antigenic changes, without the reliance on explicit modeling of hemagglutinin inhibition titers. Our approach holds immense promise to augment existing surveillance networks, offering timely insights for the development of updated, effective influenza vaccines. Consequently, NIAViD, in conjunction with other resources, could be used to support surveillance efforts and inform the development of updated influenza vaccines.