aspg-18-3774-1792515279 20261020165439 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 17 1 Golden Nancy Division of AIML, Karunya Institute of Technology and Sciences, Coimbatore, India E. Bhuvaneswari Department of Artificial Intelligence and Data Science, Panimalar Engineering College, Chennai, India Venkatesan R. Division of CSE, Karunya Institute of Technology and Sciences, Coimbatore, India 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. 2025 16 26 3774 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.170102 https://www.americaspg.com/journal/18/article/3774 https://www.americaspg.com/storage/21744648529.pdf