PURPOSE: The present study aims to identify factors related to anastomotic leakage before esophagectomy and to construct a prediction model. METHODS: A retrospective analysis of 285 patients who underwent minimally invasive esophagectomy (MIE). An absolute shrinkage and selection operator was applied to screen the variables, and predictive models were developed using binary logistic regression. RESULTS: A total of 28 variables were collected in this study. LASSO regression analysis, combined with previous literature and clinical experience, finally screened out four variables, including aortic calcification, heart disease, BMI, and FEV1. A binary logistic regression was conducted on the four predictors, and a prediction model was established. The prediction model showed good discrimination and calibration, with a C-statistic of 0.67 (95% CI, 0.593-0.743), a calibration curve fitting a 45° slope, and a Brier score of 0.179. The DCA demonstrated that the prediction nomogram was clinically useful. In the internal validation, the C-statistic still reaches 0.66, and the calibration curve has a good effect. CONCLUSIONS: When patients have aortic calcification, heart disease, obesity, and a low FEV1, the risk of anastomotic leakage is higher, and relevant surgical techniques can be used to prevent it. Therefore, the clinical prediction model is a practical tool to guide surgeons in the primary prevention of anastomotic leakage.