Objective To identify genes of lipopolysaccharide (LPS) -induced acute lung injury (ALI) in mice base on bioinformatics and machine learning. Methods The acute lung injury dataset (GSE2411, GSE111241 and GSE18341) were download from the Gene Expression Database (GEO). Differential gene expression analysis was conducted. Gene ontology (GO) analysis, KEGG pathway analysis, GSEA enrichment analysis and protein-protein interaction analysis (PPI) network analysis were performed. LASSO-COX regression analysis and Support Vector Machine Expression Elimination (SVM-RFE) was utilized to identify key biomarkers. Receiver operator characteristic curve was used to evaluate the diagnostic ability. Validation was performed in GSE18341. Finally, CIBERSORT was used to analyze the composition of immune cells, and immunocorrelation analysis of biomarkers was performed. Results A total of 29 intersection DEGs were obtained after the intersection of GSE2411 and GSE111241 differentially expressed genes. Enrichment analysis showed that differential genes were mainly involved in interleukin-17, cytokine - cytokine receptor interaction, tumor necrosis factor and NOD-like receptor signaling pathways. Machine learning combined with PPI identified Gpx2 and Ifi44 were key biomarkers. Gpx2 is a marker of ferroptosis and Ifi44 is an type I interferon-induced protein, both of which are involved in immune regulation. Immunocorrelation analysis showed that Gpx2 and Ifi44 were highly correlated with Neutrophils, TH17 and M1 macrophage cells. Conclusion Gpx2 and Ifi44 have potential immunomodulatory abilities, and may be potential biomarkers for predicting and treating ALI in mince.