Sepsis-induced acute respiratory distress syndrome (ARDS) is one of the leading causes of death in critically ill patients, and macrophages play very important roles in the pathogenesis and treatment of sepsis-induced ARDS. The aim of this study was to screen macrophage-related biomarkers for the diagnosis and treatment of sepsis-induced ARDS by bioinformatics and machine learning algorithms. A dataset including gene expression profiles of sepsis-induced ARDS patients and healthy controls was downloaded from the gene expression omnibus database. The limma package was used to screen 325 differentially expressed genes, and enrichment analysis suggested enrichment mainly in immune-related pathways and reactive oxygen metabolism pathways. The level of immune cell infiltration was analysed using the ssGSEA method, and then 506 macrophage-related genes were screened using WGCNA; 48 showed differential expression. PPI analysis was also performed. SVM-RFE and random forest map analysis were used to screen 10 genes. Three key genes, SGK1, DYSF and MSRB1, were obtained after validation with external datasets. ROC curves suggested that all three genes had good diagnostic efficacy. The nomogram model consisting of the three genes also had good diagnostic efficacy. This study provides new targets for the early diagnosis of sepsis-induced ARDS.