Many prognostic factors have been identified in acute myeloid leukemia (AML). In this study, we investigated the novel prognostic biomarkers using machine learning and cox regression models in a prospective cohort of 591 AML patients and tried to identify potential therapeutic targets based on transcriptomic data. We found that elevated red blood cell distribution width (RDW) at diagnosis was an adverse prognostic factor for AML independent of 2022 European LeukemiaNet (ELN2022) genetic risk. As a continuous variable, higher RDW was associated with shorter OS (HR 1.087, 95% CI 1.036-1.139, P<0.001) and EFS (HR 1.078, 95% CI 1.033-1.124, P<0.001). Elevated RDW returned to normal after consolidation therapy, which indicated that leukemia cells resulted in abnormal RDW. We further investigated the relationship between RDW and transcriptome in another cohort of 191 AML patients and public datasets by GSEA and CIBERSORT. We found that patients in the high RDW group were significantly enriched in the positive regulation of erythroid differentiation and inflammation-related pathways. Finally, we identified the inflammation-associated gene IL12RB2, and verified its prognostic relevance with AML patients in public databases, suggesting it as a potential therapy target.