Acute myeloid leukemia is a highly heterogeneous hematopoietic malignancy, and we constructed a prognostic signature combining disulfidptosis-related genes and ferroptosis-related genes to predict the prognosis, immunotherapy response, and drug sensitivity of acute myeloid leukemia (AML) patients. The TCGA-LAML datasets underwent random partitioning into training and validation sets. Subsequently, a prognostic risk signature was formulated using the least absolute shrinkage and selection operator algorithm. Kaplan-Meier survival analysis and receiver operating characteristic curve analysis were employed to assess the clinical significance of the signature. The results of the immune infiltration difference analyses are displayed, and the drug sensitivity analyses were used to identify potentially effective drugs for AML patients. qPCR was employed to validate the expression levels of the signature genes and compared the signature with existing signatures and mutated genes. Univariate and multivariate Cox regression analyses have underscored the signature as an autonomous prognostic risk determinant. Scrutiny into immune infiltration has unveiled significant associations: the risk score exhibits a favorable correlation with monocyte and M2 macrophage counts but an adverse correlation with resting mast cell counts. The expression patterns of immune checkpoint genes diverge between the distinct risk cohorts. Patients categorized as high-risk demonstrate enhanced benefits from cyclopamine, 443654, and 770041, whereas those classified as low-risk exhibit more pronounced advantages from cytarabine and AZD6244. The risk signature demonstrates superior prognostic accuracy compared to established signatures and mutated genes. In summary, our study may provide potential prognostic biomarkers and individualized precision therapy for AML patients.