Convolutional neural networks (CNNs) are advanced algorithms widely used for analyzing medical images and extracting clinically relevant information with unprecedented accuracy and efficiency. In this chapter, it is shown how CNNs can help detect pancreatic cancer, especially pancreatic ductal adenocarcinoma (PDAC). We have used pre-trained CNN models like VGG16, custom CNN, and a hierarchical Convolutional Neural Network (CNN) architecture. In our models, we have used several layers for filtering and pooling. In data preprocessing, conversion of multi-channel label arrays to binary format, extensive shuffling, and sampling of training data was done to ensure robust model training. Data augmentation techniques were used to improve model generalization. The algorithms learn to identify the classes of an image by performing feature extraction and data augmentation on each image. Overall, various model architectures, data preparation techniques, augmentation strategies, and training methodologies have led to the development of good deep-learning models for finding pancreatic cancer. The custom VGG16 model with augmentation gives us the best result, with a training accuracy of 93.78 and a validation accuracy of 92.04. These findings will help make better ways to find cancer early and help patients do better when fighting pancreatic cancer.