To construct an m6A-related prognostic model for glioblastoma (GBM) and identify potential prognostic biomarkers regulated by m6A. Methods: Expression, clinical phenotype, and survival data of GBM were collected from TCGA and GEO, respectively, and 21 m6A regulatory factors were obtained from m6A2Target, of which 19 intersected with genes in TCGA-GBM. First, genes with differential expression between normal and cancer tissues were calculated based on TCGA-GBM expression data. Then, consistency clustering was performed based on differentially expressed m6A regulatory factors to identify subtypes, and a prognostic model based on differentially expressed genes between subtypes was constructed and validated using a test set. Results: We obtained 21 m6A regulatory factors from m6A2Target, of which 19 intersected with the genes included in TCGA-GBM. Among them, 6 m6A regulatory factors showed significantly higher expression in GBM. Based on the differential expression of m6A regulatory factors, two subtypes, cluster1 and cluster2, were identified through consensus clustering. Furthermore, patients in cluster2 exhibited higher immune scores and stromal scores, while cluster1 showed significantly higher overall tumor purity. Survival analysis revealed poorer prognosis in cluster2. A total of 6591 genes showed significant differential expression between cluster1 and cluster2. Through univariate and multivariate Cox regression, 171 significantly prognostic-related genes were selected. The LASSO-Cox regression constructed a prognostic risk model that demonstrated good performance in both the training set (TCGA-GBM) and the testing set (GSE121720). Conclusion: Multiple m6A factors show significant differences in GBM patients, and two groups of patients based on m6A typing have different prognoses. A prognostic model based on intergroup differences in m6A can guide patients' 5-year survival rate.