Our study is aimed at constructing and validating a UPR-associated gene signature to predict HNSCC prognosis. We obtained 544 samples of RNA sequencing data and clinical characteristics from TCGA database and randomly grouped the samples into training and testing cohorts (1 : 1 ratio). After identifying 14 UPR-associated genes with LASSO and univariate Cox regression analysis, HNSCC samples were categorized into low-risk (LR) and high-risk (HR) subgroups depending on the risk score. Our analyses indicated that low-risk patients had a much better prognosis in the training and testing cohorts. To predict the HNSCC prognosis with the 14 UPR-associated gene signatures, we incorporated the UPR gene risk score, N stage, M stage, and age into a nomogram model. We further explored the sensitivity to anticancer drugs by using the IC50 analysis in two subgroups from the Cancer Genome Project database. The outcomes showed that the AKT inhibitor III and sorafenib were sensitive anticancer drugs in HR and LR patients, respectively. The immune cell infiltration analysis and GSEA provided strong evidence for elucidating the molecular mechanisms of UPR-associated genes affecting HNSCC. In conclusion, the UPR-associated gene risk score, N stage, M stage, and age can serve as a robust model for predicting prognosis and can improve decision-making at the individual patient level.