BACKGROUND: The preoperative cervical lymph node metastasis (CLNM) status in patients with papillary thyroid carcinoma (PTC) critically determines the type of lymph node dissection performed. Currently, ultrasonography is the primary method for initial CLNM screening in PTC patients. This study aims to analyze the efficacy of an ultrasonic-characteristics-based scoring system in diagnosing lymph node metastasis in PTC and construct a predictive nomogram. METHODS: The imaging findings, fine-needle aspiration (FNA) results, and surgical pathology data from 269 suspected CLNM cases at Peking University Shenzhen Hospital, spanning from July 2021 to October 2022, were retrospectively analyzed. We identified specific ultrasound characteristics and assigned scores based on our clinical experience. The diagnostic performance of the ultrasound scoring system was assessed by plotting receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). Additionally, a nomogram was developed using least absolute shrinkage and selection operator (LASSO)-logistic regression. The nomogram's discrimination was evaluated using ROC analysis, its accuracy was assessed with calibration curves, and its clinical utility was determined by decision curve analysis (DCA). RESULTS: In this study, factors such as age, sex, lymph node length, thickness, aspect ratio, shape, hilum status, echogenicity, microcalcification, cystic necrosis, blood flow pattern, and the ultrasonic score were included in the analysis. The ultrasound score had the highest (AUC = 0.914, 95% confidence interval [CI]: 0.880-0.950), with an optimal cutoff value of 2.5. A score of 3 or higher had a diagnostic sensitivity for CLNM of 81.1%, specificity of 85.2%, positive predictive value (PPV) of 83.1%, negative predictive value (NPV) of 83.4%, and Kappa value of 0.664. Subsequent LASSO regression analysis identified sex, hyperechogenicity, peripheral disordered blood flow, and the ultrasonic score as independent predictors of CLNM, which were incorporated into a logistic regression-based predictive nomogram. The model exhibited strong discriminatory performance in both the training set (AUC = 0.933, 95% CI: 0.820-0.910) and the test set (AUC = 0.958, 95% CI: 0.790-0.890) for distinguishing PTC with and without CLNM. Furthermore, calibration curves and decision curve analysis (DCA) confirmed the model's good fit and favorable clinical net benefit. CONCLUSION: The ultrasonic scoring method and the Nomogram have significant clinical utility in the preoperative assessment of CLNM in PTC, reducing unnecessary FNA procedures, and are simple and practical for clinical application. CLINICAL TRIAL NUMBER: Not applicable.