This article explores the application and future prospects of artificial intelligence (AI) in pancreatic cancer. Pancreatic cancer is a highly aggressive malignancy with poor prognosis due to late diagnosis and high invasiveness. Recent advances in AI, particularly in machine learning and deep learning, have enabled its application in early screening, diagnosis, treatment, and prognosis prediction. AI-based radiomics and deep learning models have shown promise in accurately diagnosing pancreatic cancer and predicting high-risk cases and postoperative recurrence. Additionally, AI has been integrated into pancreatic cancer surgery through three-dimensional visualization and augmented reality navigation, improving surgical precision and outcomes. AI-assisted early screening and risk prediction have shown significant potential. For example, AI models trained on endoscopic ultrasound (EUS) images have demonstrated high accuracy in distinguishing malignant from benign intraductal papillary mucinous neoplasms (IPMN). Predictive models based on electronic health records have also identified high-risk subcohorts of patients with diabetes who are at increased risk of pancreatic cancer. AI has also been used to develop risk scoring systems based on urinary biomarkers, achieving high diagnostic accuracy. In diagnosis, AI has been applied to enhance the accuracy of imaging techniques such as EUS and CT scans. Deep learning models have been developed to differentiate between pancreatic ductal adenocarcinoma and IPMN, as well as to predict the response to neoadjuvant therapy. AI has also been used to improve the accuracy of surgical planning and navigation during pancreatic cancer surgery. In postoperative management, AI has been used to predict the risk of complications such as pancreatic fistula and postoperative bleeding. AI-based models have shown high accuracy in predicting these complications, enabling early intervention and improved patient outcomes. In terms of prognosis, AI has been used to predict the likelihood of recurrence and survival rates in pancreatic cancer patients. AI models have demonstrated superior performance compared to traditional statistical methods, offering more accurate predictions. Despite these advancements, challenges remain, including the interpretability of AI models, generalization across diverse datasets, and ethical considerations. Future research should focus on improving model interpretability, enhancing generalization, and establishing ethical guidelines for AI use in pancreatic cancer. The integration of multi-omics data analysis holds promise for personalized treatment strategies, ultimately improving therapeutic outcomes and survival rates.This article explores the application and future prospects of artificial intelligence (AI) in pancreatic cancer. Pancreatic cancer is a highly aggressive malignancy with poor prognosis due to late diagnosis and high invasiveness. Recent advances in AI, particularly in machine learning and deep learning, have enabled its application in early screening, diagnosis, treatment, and prognosis prediction. AI-based radiomics and deep learning models have shown promise in accurately diagnosing pancreatic cancer and predicting high-risk cases and postoperative recurrence. Additionally, AI has been integrated into pancreatic cancer surgery through three-dimensional visualization and augmented reality navigation, improving surgical precision and outcomes. AI-assisted early screening and risk prediction have shown significant potential. For example, AI models trained on endoscopic ultrasound (EUS) images have demonstrated high accuracy in distinguishing malignant from benign intraductal papillary mucinous neoplasms (IPMN). Predictive models based on electronic health records have also identified high-risk subcohorts of patients with diabetes who are at increased risk of pancreatic cancer. AI has also been used to develop risk scoring systems based on urinary biomarkers, achieving high diagnostic accuracy. In diagnosis, AI has been applied to enhance the accuracy of imaging techniques such as EUS and CT scans. Deep learning models have been developed to differentiate between pancreatic ductal adenocarcinoma and IPMN, as well as to predict the response to neoadjuvant therapy. AI has also been used to improve the accuracy of surgical planning and navigation during pancreatic cancer surgery. In postoperative management, AI has been used to predict the risk of complications such as pancreatic fistula and postoperative bleeding. AI-based models have shown high accuracy in predicting these complications, enabling early intervention and improved patient outcomes. In terms of prognosis, AI has been used to predict the likelihood of recurrence and survival rates in pancreatic cancer patients. AI models have demonstrated superior performance compared to traditional statistical methods, offering more accurate predictions. Despite these advancements, challenges remain, including the interpretability of AI models, generalization across diverse datasets, and ethical considerations. Future research should focus on improving model interpretability, enhancing generalization, and establishing ethical guidelines for AI use in pancreatic cancer. The integration of multi-omics data analysis holds promise for personalized treatment strategies, ultimately improving therapeutic outcomes and survival rates.