Pancreatic cancer remains an immense challenge in oncology due to its rising incidence, dismal survival rates, and substantial public health and socioeconomic impact. Typically diagnosed at advanced stages, lacking cost-effective screening and early detection methods, and with predominantly palliative treatments, pancreatic cancer poses significant hurdles. In this context, machine learning, a facet of artificial intelligence, utilizes computational and statistical methods to unveil patterns within datasets. Its application has demonstrated remarkable progress in cancer research and clinical oncology, particularly for pancreatic cancer. Deep learning, a subset of machine learning, harnesses the power of multilayered network techniques. Its deployment spans the analysis of electronic health records, radiographic images, digitized histopathology, molecular omics, and other data. Notably, deep learning models have showcased their ability to enhance risk prediction, detection, diagnosis, staging, and prognostication across diverse malignancies, including pancreatic cancer, achieving heightened accuracy. Furthermore, deep learning’s analysis of clinical, pathological, and molecular data has facilitated the prediction of tumor recurrence and response to therapy in patients with pancreatic cancer. Looking ahead, the continuous advancement of machine learning and deep learning methods for the analysis of multimodal datasets holds the potential to revolutionize our capabilities in detecting, diagnosing, and managing pancreatic cancer. Such developments are poised to exert a positive impact on patient clinical outcomes, while simultaneously alleviating the socioeconomic burden associated with this formidable malignancy.