Pancreatic cancer is one of the most lethal malignancies, characterized by difficulties in early diagnosis, limited therapeutic options, and generally poor patient prognosis. In recent years, immunotherapy has provided new opportunities for the treatment of pancreatic cancer; however, its clinical efficacy has been substantially constrained by the complex tumor microenvironment (TME) and immune evasion mechanisms. With the rapid advancement of artificial intelligence (AI) technologies, AI has demonstrated great potential in the early detection of pancreatic cancer, prediction of immunotherapeutic responses, and design of personalized treatment strategies. This review systematically summarizes the latest advances in the application of artificial intelligence in pancreatic cancer immunotherapy, with a particular focus on key AI assisted technologies, including tumor immune microenvironment characterization, prediction of genetic mutation profiles, nanomedicine design, and dynamic monitoring of therapeutic responses. By integrating single cell sequencing and multi-omics data analyses, we discuss how AI can effectively address critical bottlenecks in immunotherapy. In addition, this article analyzes current technical challenges and future development trends, aiming to provide a theoretical foundation and practical guidance for achieving precision immunotherapy in pancreatic cancer and to promote clinical translation and application in this field.