Developed a deep learning approach for diagnosing Acute Lymphoblastic Leukemia (ALL) using peripheral blood smear images. Utilizing a convolutional neural network (CNN), the model achieved 99% training accuracy and 97.09% validation accuracy, effectively distinguishing between ALL subtypes and benign hematogone cases. This solution enhances diagnostic accuracy and efficiency, supporting critical treatment decisions.