Background: The extensive study of clinical health systems is creating a paradigm for the newest computer-based systems that are emerging. Pancreatic cancer, which cannot be allowed to be treated efficiently once diagnosed and is frequently unanticipated due to its position in the belly below the stomach, is one of the most prevalent tumors that is believed to be irreversible. Biological therapies, sometimes referred to as immunotherapies or targeted therapies, are used to treat pancreatic cancer in order to control hormone pathways, target certain cancer cells, or strengthen the immune system. Method: Pancreatic cancer is the fourth leading cause of cancer deaths, and there currently is no reliable modality for the early detection of this disease. Here, identifies cancer-specific promoter DNA methylation of BNC1 and ADAMTS1 as a promising biomarker detection strategy meriting investigation in pancreatic cancer. Nanoparticles directly target tumor cells, allowing their detection and removal. It also can be engineered to carry specific payloads, such as drugs or contrast agents, and enhance the efficacy and precision of cancer treatment. This study develops a unique cascaded fully convolutional neural network (CFCNN) with Hybrid Krill Herd African Buffalo Optimization (HKH-ABO) mechanism for early pancreatic computed tomography (CT) image classification of pancreatic cancer. A new Wienmed filter is created for pre-processing the noisy CT image content after the system is successfully trained on pancreatic CT pictures. In addition, the proposed CFCN with the HKH-ABO pathway distinguishes between pancreatic cancerous and non-pancreatic cancerous forms of the disease. Results: The accuracy of the CFCNN for the analysis of pancreatic cancer was 98.87%, showing that the various volumes of the 3DIRCAD datasets analyzed had a combined accuracy rate of 99% for training and 99% for testing. Conclusion: The combination of advanced biomarker identification, BNC1 and ADAMTS1 methylation, and nanoparticle-based targeting further enhances the precision and efficacy of pancreatic cancer diagnosis and treatment. As a result, advancements in medical study are steadily going in the direction of the installation of automation machines that determine the phases of cancers and, if directly touched, provide better guidance and therapy.