Background <p>The early diagnosis and prognosis of head and neck tumors are crucial for enhancing patients’ quality of life. This study focuses on angiogenic genes to develop prognostic and diagnostic models, offering a novel strategy to improve patient outcomes and increase the early diagnosis rate of HNSCC.</p> Methods <p>Samples were clustered based on MSI, SCNA, and mTMB. A vascularization-related diagnostic and prognostic scoring model was constructed through the analysis of differentially expressed genes (DEGs). The model was validated using univariate and multivariate Cox proportional hazards analyses with data from the International Cancer Genome Consortium (ICGC). Subsequently, samples were grouped according to model scores, and immune infiltration analysis was conducted. Immune profiling, combined with narrow band imaging (NBI), was employed to verify the diagnostic model.</p> Results <p>Cluster analysis based on MSI, SCNA, and mTMB burden values effectively stratified HNSCC into three distinct types. An angiogenesis-related prognostic model was developed from differential gene analysis to assess patient outcomes. Validation using an external database (ICGC dataset) confirmed the model’s robust performance. Following the stratification of TCGA samples into high-risk and low-risk groups based on risk scores, significant differences were observed in expression data and immune cell infiltration between the two cohorts. Furthermore, after adjustments using both univariate and multivariate Cox regression analyses, the risk score index emerged as an independent prognostic factor. Subsequent validation using Narrow Band Imaging (NBI) combined with immunohistochemistry confirmed the model’s utility in diagnosing early-stage head and neck tumors.</p> Conclusion <p>The model holds potential for application in the diagnostic and prognostic evaluation of early-stage head and neck tumors.</p>