Abstract: Islet transplantation as an effective treatment for diabetes mellitus, has increasingly attracted attention in recent years. However, it faces challenges such as a shortage of donors, loss of islets during isolation and transplantation, and the need for lifelong immunosuppression. With the rapid development of multi-omics technologies and the widespread application of machine learning algorithms, researchers have begun to explore how to use these innovative technologies to improve the success rate of islet transplantation and improve the quality of life for patients. Machine learning has demonstrated unique advantages in data integration, pattern recognition, and predictive accuracy, thereby supporting precise prediction and personalized treatment strategies. The integration of multi-omics and machine learning holds the potential to revolutionize diabetes mellitus management and advance precision medicine by optimizing donor-recipient matching and personalized immunosuppression protocols. Therefore, this article reviews the current applications of multi-omics and machine learning in islet transplantation, explores their potential impact on diabetes mellitus treatment, and looks forward to future research directions, aiming to provide references for optimizing islet transplantation as a treatment for diabetes mellitus.