【Abstract】Objective: To develop and validate a multimodal data-based predictive model for central lymph node metastasis (CLNM) in patients with medullary thyroid carcinoma (MTC) and evaluate its clinical significance. Methods: We retrospectively analyzed clinical-pathological data, preoperative imaging features, and hematological parameters of 104 MTC patients treated at the First Affiliated Hospital of Nanjing Medical University between January 2017 and May 2025. Patients were classified into CLNM-positive (n=55) and CLNM-negative (n= 49) groups based on pathological findings. Significant predictors of CLNM were identified through univariate and multivariate logistic regression analyses. A nomogram was constructed, and model performance was assessed using the receiver operating characteristic (ROC) curve (discrimination), calibration curve (calibration), and decision curve analysis (DCA; clinical utility). Internal validation was performed via bootstrap resampling. Results: Compared to the CLNM-negative group, CLNM-positive patients showed significant differences in gender (P = 0.001), US tumor morphology (regularity; P < 0.001), US tumor margin (circumscribed status; P < 0.001), serum carcinoembryonic antigen (CEA) levels (P = 0.006), and serum calcitonin (CT) levels (P < 0.001). Multivariate analysis identified male gender (OR = 6.63, 95% CI: 2.04–21.57; P = 0.002), non-circumscribed US margins (OR = 10.49, 95% CI: 2.79–39.37; P < 0.001), and elevated serum CT (OR = 1.25 per 1-unit increase, 95% CI: 1.10–1.42; P < 0.001) as independent risk factors for CLNM. The nomogram integrating these factors demonstrated excellent discrimination (AUC = 0.873, 95% CI: 0.808–0.939), with good calibration and clinical utility on DCA. Bootstrap validation confirmed model stability (AUC = 0.874, 95% CI: 0.865–0.879). Conclusion: A multimodal model incorporating gender, US tumor margin status, and serum CT levels effectively predicts CLNM risk in MTC patients, providing a valuable tool for clinical decision-making.