Head and neck cancers represent a clinically and biologically heterogeneous group of malignancies, in which treatment decisions are still largely driven by anatomical staging despite substantial variability in outcomes. In recent years, the integration of multi-omics data, including transcriptomics and radiomics, has provided new insights into tumor biology and enabled more refined patient stratification beyond conventional clinical parameters. Gene expression-based classifications have demonstrated both prognostic and predictive value, while imaging-derived features offer a non-invasive approach to capture tumor heterogeneity and complement molecular profiling.