This study introduces a novel deep learning-driven multi-layer digital twin framework, underpinned by the Model-Integration-Optimization-Testing (MIOT) methodology, to advance precision oncology in cancer diagnosis. The innovation lies in integrating multi-layered data, including molecular, clinical, and imaging modalities, into a patient-specific digital twin ecosystem. By combining deep learning with the MIOT framework, the proposed approach enables dynamic and predictive modelling tailored to individual patient profiles, facilitating simulations of tumor progression, diagnostic insights, and personalized treatment optimization. Pre-processing pipelines standardize the heterogeneous data, while convolutional and Recurrent Neural Networks        (RNN) extract high-level features from imaging and sequential data, respectively. The MIOT framework ensures a systematic design process: deep learning architectures like U-Net, DenseNet, and transformers are employed for tasks such as tumor segmentation, classification, and survival prediction. Data integration pipelines connect the digital twin seamlessly with clinical diagnostic tools to ensure interoperability. Multi-objective optimization algorithms, including evolutionary strategies and reinforcement learning, guide the digital twin in recommending personalized diagnostic and therapeutic pathways. State-of-the-art performance is demonstrated by rigorous validation on benchmark datasets, which yielded 96.3% diagnosis accuracy, 94.8% sensitivity, and 95.6% specificity across many tumor subtypes.