This study explores the potential of integrating DNA microarray technology for gene expression profiling with machine learning to facilitate cancer classification. The main aim is to identify novel cancer types and accurately assign tumors to established categories, concentrating on acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) patients. In recent times, progress in cancer research has tapped into gene expression data to unravel the molecular complexities of cancer. This approach has a unique capability for differentiating diverse types of cancer based on gene expression patterns independent of a priori knowledge. Predictive models that are based on machine learning algorithms have been developed which can classify cancers very precisely, hence making it useful in early detection and personalized treatment. In the context of acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), this study showcases how gene expression monitoring, in conjunction with machine learning, has been instrumental in distinguishing these leukemia subtypes with remarkable accuracy. The unique gene expression signatures of AML and ALL have paved the way for more efficient patient management and individualized therapeutic strategies.