The clinical manifestation of aortic dissection (AD) is complex and varied, making early diagnosis crucial for patient survival. This study aimed to identify immune-related markers to establish a nomogram model for AD diagnosis. Three datasets from GEO-GSE52093, GSE147026 and GSE153434-were combined and used for identification of immune-related causative genes using weighted gene co-expression network analysis, and 136 immune-related genes were obtained. Then, 15 pivotal genes were screened by the protein-protein interaction network. Through machine learning including the Least Absolute Shrinkage and Selection Operator algorithm, random forest algorithm, and multivariate logistic regression, four key feature genes were obtained-