Objective: We tried to identify novel molecular subtypes of acute myeloid leukemia (AML) associated with histone methylation and established a relevant scoring system to predict treatment response and prognosis of AML. Methods: Gene expression data and clinical characteristics of patients with AML were obtained from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. Molecular subtyping was carried out by consensus clustering analysis, based on the expression of 24 histone methylation modification regulators (HMMRs). The clinical and biological features of each clustered pattern were taken into account. The scoring system was constructed by using differential expression analysis, Cox regression method and lasso regression analysis. Subsequently, the scoring system in the roles of prognostic and chemotherapeutic prediction of AML were explored. Finally, an independent GSE dataset was used for validating the established clustering system. Results: Two distinct subtypes of AML were identified based on the expression of the 24 HMMRs, which exhibited remarkable differences in several clinical and biological characteristics, including HMMRs expression, AML-M0 distribution, NPM1 mutation, tumor mutation burden, somatic mutations, pathway activation, immune cell infiltration and patient survival. The scoring system, M-RiskScore, was established. Integrated analysis demonstrated that patients with the low M-RiskScore displayed a prominent survival advantage and a good response to decitabine treatment, while patients with high M-RiskScore have resistance to decitabine, but they could benefit from IA regimen therapy. Conclusion: Detection of HMMRs expression would be a potential strategy for AML subtyping. Meanwhile, targeting histone methylation would be a preferred strategy for either AML-M0 or NPM1 mutant patients. M-RiskScore was a useful prognostic biomarker and a guide for the choice of appropriate chemotherapy strategy.