BACKGROUND: Abdominal aortic aneurysm (AAA) is a common degenerative vascular disease characterized by progressive dilation of the abdominal aorta, which poses a life-threatening risk upon rupture. Lactate, a key metabolic byproduct and immunomodulatory molecule, plays a crucial role in regulating immune cell functions in various inflammatory diseases. However, the specific involvement of lactate metabolism in the pathogenesis of AAA remains poorly understood. This study aims to identify lactate metabolism-related gene signatures associated with AAA and to elucidate their potential roles and underlying mechanisms in disease progression. METHODS: Transcriptomic datasets GSE57691, GSE183464, and GSE237230 were obtained from the Gene Expression Omnibus (GEO) database. Lactate metabolism-related genes were retrieved from the Molecular Signatures Database (MSigDB). Weighted Gene Co-expression Network Analysis (WGCNA) and the Limma R package were employed to identify key gene modules associated with AAA and detect differentially expressed genes (DEGs) between AAA and control groups, respectively. Overlapping genes were subjected to functional enrichment analysis and protein-protein interaction (PPI) network construction. Three distinct machine learning algorithms were applied to screen for potential diagnostic biomarkers. Upon validation, a nomogram was constructed based on the selected biomarkers. Immune infiltration and single-cell RNA analysis were performed to characterize the immune microenvironment and investigate the association between immune cell subsets and AAA-related biomarkers. Finally, the expression patterns of the identified biomarkers were validated using a murine model of AAA. RESULTS: A total of 3336 AAA-related module genes, 2651 DEGs between AAA and controls, and 364 lactate metabolism-related genes were identified. Among these, 29 genes were recognized as lactate metabolism-related DEGs associated with AAA. Functional enrichment analysis revealed significant enrichment in pathways related to oxidative phosphorylation and energy metabolism. SLC25A4, HBB, and STAT4 were identified as candidate biomarkers for AAA. Immune infiltration analysis revealed distinct immune profiles between AAA and control groups. Single-cell mRNA analysis demonstrated that SLC25A4 is predominantly expressed in adventitial cells and fibroblasts in AAA, HBB is expressed across multiple immune cell subsets, and STAT4 is mainly expressed in T cells. Gene Set Enrichment Analysis indicated that these biomarkers are involved in biological processes related to T cell activation and T cell differentiation. These findings were further validated in a murine model of AAA. CONCLUSIONS: The identification of lactate metabolism-related biomarkers and the comprehensive characterization of the immune microenvironment in AAA offer novel insights that may contribute to the development of targeted therapeutic strategies for AAA.