OBJECTIVE: This study was aimed at analyzing the correlation of serum γKlotho with the long-term prognosis of multivessel coronary artery disease (MVD) and develop a predictive model to predict an accurate long-term prognosis. METHODS: We enrolled 969 MVD patients and classified them into three groups: training (n = 552), internal validation (n = 224), and external validation (n = 193) groups, respectively. The training group data helped in establishing a prognostic model. Univariable Cox regression analyses were conducted using serum and clinical γKlotho levels. Thereafter, the least absolute shrinkage and selection operator (LASSO) regression model was utilized for optimizing feature selection. Additionally, we constructed a prognosis prediction nomogram with multivariate Cox regression results by incorporating features screened with the LASSO model. Moreover, the as-constructed prognosis model's discriminability, consistency, and clinical usefulness were assessed by receiver operating characteristic (ROC) curve, C-index, decision curve analysis (DCA), and calibration plot analysis, respectively. RESULT: Included in our prognosis prediction nomogram, the predictors of the long-term prognosis of MVD patients were age, BMI, diabetes mellitus (DM), antiplatelets, LDL-C, LVDD, and serum γKlotho level. Consequently, this nomogram displayed high discriminability, according to ROC and C-index analyses. Moreover, according to the calibration plot, the nomogram's probabilities exhibited high consistency with the actual levels. Based on DCA, this nomogram displayed good clinical usefulness. CONCLUSION: Higher serum γKlotho levels correlated with the poor prognosis of MVD patients, our predictive model exhibited better predictive ability and clinical usefulness.