Head and neck squamous cell carcinoma (HNSCC) has become the sixth most common malignant disease worldwide and is associated with high mortality, with an overall 5-year survival rate of less than 50%. Recent studies have demonstrated that aberrantly expressed m6A regulators are involved in multiple biological and pathological processes, including cancers, but the specific mechanisms of m6A regulators in HNSCC are not well elucidated. In this study, we adopted The Cancer Genome Atlas (TCGA)-HNSCC database and performed a consensus clustering analysis to classify the HNSCC samples. Least absolute shrinkage and selection operator (LASSO) regression was applied to construct an m6A signature-based HNSCC risk prediction model. Cell type identification based on estimating relative subsets of RNA transcripts (CIBERSORT) algorithms was adopted to evaluate the immune cell infiltration level in the tumor microenvironment. Based on the expression of m6A regulators in HNSCC, we identified two clusters, cluster 1 (C1) and cluster 2 (C2). C2 showed a better prognosis than C1 and was mainly enriched in the HIPPO, MYC, NOTCH, and NRF signaling pathways. We constructed an m6A signature-based risk score model and classified patients into high- and low-risk score subgroups. The high-risk-score group showed poor clinical characteristics, higher immune infiltration levels, higher chemokine and chemokine receptor expression levels, and lower immune checkpoint gene expression than the low-risk-score subgroup. In conclusion, our comprehensive analysis suggests that the m6A signature-based risk score might function as a good prognostic predictor. Our study may provide novel therapeutic clues and help predict the prognosis of HNSCC.