Pancreatic cancer often has an insidious onset and difficulties in treatment， with various limitations in early diagnosis and treatment. This article reviews the application of Mendelian randomization （MR） in exploring the risk factors for pancreatic cancer， with a special focus on the causal relationships of factors such as gut microbiota， lifestyle， and metabolic diseases. Leveraging data from large-scale genome-wide association studies （GWAS）， MR analysis has revealed several biomarkers associated with the risk of pancreatic cancer. The two-sample MR approach is commonly used in current research， including the methods such as Inverse Variance Weighted， Weighted Median， and MR-Egger， which helps to explain the causal network of the disease from a genetic perspective. While MR strategy provides a new perspective for understanding the etiology of pancreatic cancer， caution is still needed in data synthesis， selection of instrumental variables， and pleiotropy assessment. The use of emerging analytical models such as BWMR， CAUSE， and MVMR offers new possibilities for the comprehensive evaluation of multiple risk factors and their interaction. In the future， with the combination of these methods and the ever-increasing genetic epidemiological data， MR analysis is expected to provide more solid evidence for identifying potential therapeutic targets for pancreatic cancer and formulating prevention strategies.