Objective To evaluate the predictive value of dual-modality ultrasound， incorporating conventional ultrasound and ultrasound elastography， in combination with S-Detect for cervical lymph node metastasis （CLNM） in patients with papillary thyroid carcinoma （PTC）. Methods A retrospective analysis was conducted on the clinical data of 135 patients diagnosed with PTC who received treatment at the First Affiliated Hospital of Shihezi University between November 2023 and August 2024. For all patients， clinical baseline characteristics， conventional ultrasound findings， ultrasound elastography results， and S-Detect analysis data were collected. Independent predictors of CLNM in PTC were identified， and predictive models were developed. Receiver operating characteristic （ROC） curves were generated to compare the area under the curve （AUC） of the models. The most effective predictive model was selected to construct a risk probability nomogram， and the predictive performance and clinical applicability of this nomogram were subsequently evaluated. Results Age， maximum nodule diameter， boundary characteristics， capsular invasion， transverse-sectional morphological findings assessed by S-Detect， and ECI-based elasticity grading were identified as independent predictors of CLNM in PTC （all P < 0.05）. The AUC of the predictive model constructed using these six variables was 0.890 （95%CI： 0.835 ~ 0.945）. The calibration curve demonstrated strong agreement between predicted and observed outcomes， and decision curve analysis indicated that the nomogram provided a favorable net clinical benefit within a threshold probability range of 2% to 91.5%. Conclusions Age， maximum nodule diameter， boundary characteristics， capsular invasion， sonographic features assessed by S-Detect in the transverse plane， and ECI-based elasticity grading are independent predictors of CLNM in PTC. A nomogram model incorporating these parameters demonstrates effective performance in predicting the likelihood of CLNM.