Objective To detect the bile acid profile in serum based on liquid chromatography-tandem mass spectrometry, and construct a combined biomarker diagnostic model for differentiating acute myocardial infarction (AMI) from unstable angina (UA). Methods A total of 180 patients with acute coronary syndrome who visited Huludao Central Hospital between August 2023 and February 2024 were randomly selected, and there were 117 patients with UA and 63 patients with AMI. Using liquid chromatography-tandem mass spectrometry, 15 bile acid subtypes in serum were detected. Orthogonal partial least squares discriminant analysis was used to compare the serum bile acid metabolic profiles of the subjects. Differences in metabolites were screened based on a significance level of P<0.05 and variable importance in projection (VIP)>1. Multiple logistic regression analysis was performed to construct a diagnostic model for differentiating AMI from UA, and the diagnostic performance of the model was evaluated using receiver operating characteristic (ROC) curve and other statistical methods. Results The differential bile acid biomarkers in the serum of UA and AMI patients included glycodeoxycholic acid, glycochenodeoxycholic acid (GCDCA), deoxycholic acid (DCA), glycocholic acid, and aurodeoxycholic acid (TDCA) (P<0.05, VIP>1). A binary logistic stepwise regression analysis showed that three bile acid biomarkers (GCDCA, DCA, and TDCA) and three common biochemical indicators (aspartate aminotransferase, creatine kinase, and total bile acid) were factors differentiating AMI from UA (P<0.05). The area under the ROC curve of the model was 0.986 [95% confidence interval (0.973, 0.999), P<0.001], demonstrating a good diagnostic performance. Conclusions GCDCA, DCA, and TDCA can serve as potential biomarkers for distinguishing AMI from UA. The model combining these three bile acids with aspartate aminotransferase, creatine kinase, and total bile acid can effectively identify AMI.