A vital component of patient care is the diagnosis of blood cancer, which necessitates prompt and correct classification for efficient treatment planning. The limitations of subjectivity and different levels of skill in manual classification methods highlight the need for automated systems. This study improves blood cancer cell identification and categorization by utilizing deep learning, a subset of artificial intelligence. Our technique uses bespoke U-Net, MobileNet V2, and VGG-16, powerful neural networks to address problems with manual classification. For the purposes biomedical image segmentation U-Net architecture is used, MobileNet V2 is used for lightweight neural network model design and VGG-16 is used for image classification. A hand-picked dataset from Taleqani Hospital in Iran is used for the rigorous training, validation, and testing of the suggested models. The dataset is refined using denoising, augmentation, and linear normalisation, which improves model adaptability. The results show that the MobileNet V2 model outperforms related studies in terms of accuracy (97.42%) when it comes to identifying and categorizing blast cells from acute lymphoblastic leukemia. This work offers a fresh approach that adds to artificial intelligence's potentially revolutionary potential in medical diagnosis.