<p>Acute myocardial infarction (AMI) continues to pose a substantial risk to human lives worldwide. Endoplasmic reticulum stress (ERS) is increasingly recognized as one of the potential mechanisms of myocardial injury following AMI. The primary goal of this study is to investigate the correlation between ERS and AMI through machine learning-based bioinformatics analysis, explore key genes, and conduct in vivo and in vitro experimental validation. We performed differential analysis and Weighted Gene Co-expression Network Analysis (WGCNA) on gene expression data from the GEO database (GSE62646). The intersection with ERS-related genes (ERSRGs) was taken to obtain AMI-ERS-related genes (MIEGs), and machine learning algorithms were further used to identify key genes (Hubs) from the MIEGs. The validation set GSE59867 was used to assess the expression levels and predictive capabilities of the Hubs for AMI. An AMI rat model was established to detect the mRNA and protein expression levels of the Hubs. The protein inhibitor of the key gene FURIN was used to treat H9C2 cells under oxygen–glucose deprivation (OGD) to explore the effects of FURIN on ERS and apoptosis. Bioinformatics analysis identified 27 MIEGs, and machine learning further determined 5 Hubs highly associated with AMI and ERS: RELA, FURIN, ERGIC3, TPP1, and BGLAP. The expression of these Hubs was significantly elevated in AMI patients within both the training and validation sets, and the area under the curve (AUC) indicated good diagnostic value. Our experiments confirmed that the mRNA levels of Furin and RelA were significantly elevated in AMI rats. Furin protein was increased in AMI rats and OGD H9C2. Furin inhibitor could alleviate OGD-induced ERS and apoptosis in H9C2. Our study demonstrates that Hubs play a pivotal role in myocardial infarction. Notably, Furin and its mediated ERS and apoptosis are significant in the pathogenesis of AMI, potentially serving as target for AMI diagnosis and treatment.</p>