Non–ST-elevation acute coronary syndrome (NSTE-ACS) remains a diagnostic challenge, as a proportion of patients do not present with obstructive coronary lesions. Coronary computed tomography angiography (CCTA) has emerged as a non-invasive tool for coronary assessment, and integrating artificial intelligence (AI) may enhance its diagnostic accuracy. This study evaluates a machine learning (ML) model using a learned fusion approach to identify culprit lesions in high-risk NSTE-ACS patients. This study is a sub-analysis of a prospective, multicenter trial including patients with high-risk NSTE-ACS who underwent CCTA, followed by ICA and fractional flow reserve (FFR) assessment in every intermediate stenosis. An ML framework was developed to analyze 2 orthogonal CCTA views of each coronary segment and classify them as culprit or non-culprit, with ICA +/- FFR as gold standards. The model was trained using 5-fold cross-validation and compared against 5 baseline methods, including conventional feature extraction and FFR-CT. Among 80 patients, 514 coronary segments were analyzed, with 63 (12.3%) labeled as culprit. The learned fusion model achieved a sensitivity of 0.55 ± 0.14, specificity of 0.93 ± 0.05, and F1-score of 0.53 ± 0.11. The AUC was 0.84 ± 0.06, matching the performance of FFR-CT (AUC of 0.82 ± 0.08). Our findings demonstrate that the learned fusion approach, based on combining two orthogonal views, achieved a performance level comparable to that of FFR-CT, as shown by the AUC of both techniques. These results confirm that AI-driven CCTA analysis could enhance clinical decision-making in high-risk NSTE-ACS patients, warranting further validation of this method in larger cohorts. Graphical abstract CCTA: coronary computed tomography angiography, FFR: fractional flow reserve, ICA: invasive coronary angiogram, NSTE-ACS: non-ST-elevation acute coronary syndrome PPV: positive predictive value