Head and neck squamous cell carcinoma (HNSCC) is a major clinical challenge due to its aggressive nature and poor prognosis in advanced stages. Late detection, often due to delayed diagnosis, limits treatment success. This study investigates non-invasive diagnostic methods to identify early-stage molecular biomarkers using a proteogenomic approach. We analyzed urine samples from 19 male HNSCC patients and identified 1427 proteins by mass spectrometry. Of these, 730 overlapped with healthy controls, highlighting prognostic markers such as RNASE1, LRG1 and CD44. Machine learning techniques, including principal component analysis and partial least squares discriminant analysis, distinguished HNSCC patients from controls and revealed unique proteomic signatures. Pathogenic variants such as GAA p.(Trp746Cys) and SIAE p.(Pro210Leu) were found to be potential indicators of advanced disease. Functional analyzes linked the identified proteins to important tumor-related processes, including epithelial-mesenchymal transition and neutrophil degranulation. These results support urinary proteomics as a promising non-invasive diagnostic tool for early detection of HNSCC and disease monitoring. Future research should validate these biomarkers in larger, more diverse cohorts to improve clinical applicability.