Pancreatic cancer maintains to pose a considerable health trouble owing to it’s behind schedule analysis and multiplied mortality rate. Conventional diagnostic procedures, predominantly reliant on imaging strategies, regularly inadequately identify early-stage cancer. A singular category technique employing deep learning is introduced to triumph over this constraint, concentrating on genetic information derived from blood and urine samples as opposed to imaging modalities which includes CT scans. The suggested method makes use of artificial Neural Networks (ANN) to have a look at molecular-stage facts, thereby improving diagnosis accuracy. The dataset includes genetic profiles obtained from patient samples, facilitating a strong type framework. The ANN architecture has an input layer that acquires genetic data, several hidden layers that analyze complicated patterns, and an output layer that affords categorization outcomes. This method exceeds conventional machine learning techniques, like support Vector Machines (SVM) and conventional neural networks, by improving accuracy and adaptability. The approach improves detection performance by utilizing advanced neural networks and incorporating constant values for optimization. The focus on genetic markers enables early and correct prognosis, main to tailored treatment techniques. This methodology represents a sizeable advancement in pancreatic most cancers detection, providing a more dependable and effective alternative to modern methods.