Background: We aimed to investigate the predictive value of recently updated ACEF II score on major adverse cardiac and cerebrovascular events (MACCE) in patients with multi-vessel coronary artery disease (MVCAD) undergoing one-stop hybrid coronary revascularization (HCR).Methods: Patients with MVCAD undergoing one-stop HCR were retrospectively recruited from March 2018 to September 2020. Several prediction risk models, including ACEF II score, were calculated for each patient. Kaplan-Meier curve was used to evaluate freedom from all-cause death and MACCE survival rates. Differences of prediction performance among risk scores for predicting MACCE were compared by receiver operating characteristic (ROC) curve. Results: According to the ACEF II score, a total of 120 patients undergoing one-stop HCR were assigned to low-score group (80 cases) and high-score group (40 cases). During the median follow-up time of 18 months, the incidence of MACCE in the low-score group and high-score group were 8.8% and 37.5%, respectively (p < 0.001); and the mortality rate of the two were 2.5% and 12.5%, respectively (p < 0.05). Moreover, the cumulative freedom from mortality (97.5% vs. 86.8%, p < 0.05) and MACCE (75.2% vs. 52.8%, p < 0.001) survival rates in the high-score group were significantly lower than in the low-score group. According to the Cox proportional hazards regression, the ACEF II score was an independent prognostic indicator for MACCE with hazards ratio (HR) 2.24, p = 0.003. The ROC curve analysis indicated that the areas under the curve (AUC) of MACCE from the ACEF II score was 0.740 (p < 0.001), while the AUC of MACCE from the SYNTAX score II CABG was 0.621 (p = 0.070) and the AUC from the EuroSCORE II was 0.703 (p < 0.001). Thus, the accurate predictive value of ACEF II score was similar to the EuroSCORE II but much higher than the SYNTAX score II CABG. Conclusion: The updated ACEF II score is a more convenient and validated prediction tool for MACCE in patients with MVCAD undergoing one-stop HCR comparing to other risk models.