In this study, we applied an artificial intelligence–driven diagnostic framework to analyze gene expression profiles of patients with acute lymphoblastic leukemia. By leveraging advanced computational techniques, the model was able to identify distinct molecular signatures and stratify patients according to their genomic subtypes. The findings demonstrate that AI-assisted genomic classification has the potential to enhance diagnostic precision and enable earlier identification of clinically relevant subgroups. This approach may support more tailored therapeutic strategies and represents an important step toward the integration of precision medicine in the management of acute lymphoblastic leukemia