Abstract Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease. Recent evidence suggests that the pathogenesis of IPF may involve abnormalities in mitochondrial energy metabolism. This study aimed to identify mitochondrial energy metabolism related differentially expressed genes (MEMRDEGs) and to elucidate their potential mechanistic involvement in IPF. We employed a multistep bioinformatics approach, including data extraction from the Gene Expression Omnibus database, removal of batch effects, and normalization and differential gene expression analyses. We then conducted Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment, and gene set enrichment analyses. A protein-protein interaction network was constructed from the STRING database, and hub genes were identified. Receiver operating characteristic curve analysis was performed to evaluate immune infiltration. Our integrated analysis of IPF datasets identified 25 MEMRDEGs. Nine hub genes emerged as central to mitochondrial energy metabolism in IPF. COX5A, EHHADH, and SDHB are potential biomarkers for diagnosing IPF with high accuracy. Single-sample gene set enrichment analysis revealed significant differences in the abundances of specertainfic immune cell types between IPF samples and controls. In conclusion, COX5A, EHHADH, and SDHB are potential biomarkers for the high-accuracy diagnosis of IPF. These findings pave the way for further investigations into the molecular mechanisms underlying IPF.