Objective To investigate the predictive value of integrating clinical pathological characteristics with genetic testing for occult lymph node metastasis （OLNM） in patients with papillary thyroid microcarcinoma （PTMC）. Methods A total of 104 PTMC patients admitted to our hospital between May 2023 and May 2025 were included in the study. All patients showed no evidence of suspicious lymph node metastasis on preoperative imaging and underwent standard thyroidectomy with central lymph node dissection. Based on postoperative pathological confirmation of central lymph node metastasis status， patients were classified into an OLNM-positive group （n = 53） and an OLNM-negative group （n = 51）. Baseline characteristics， clinicopathological features， BRAF^V600E gene mutation status， and TERT promoter mutation status were compared between the two groups. To identify factors independently associated with OLNM in PTMC patients， multivariate logistic regression analysis was conducted. The area under the receiver operating characteristic curve （AUC） was utilized to assess the predictive performance of a combined model incorporating clinical， pathological， and genetic features for OLNM. Results Compared with the OLNM-negative group， the OLNM-positive group exhibited significantly higher preoperative thyroid-stimulating hormone （TSH） levels （P < 0.05）. Moreover， the OLNM-positive group demonstrated significantly greater proportions of tumors with diameter > 0.5 cm， multifocality， microcalcifications， capsule invasion， extrathyroidal extension， T3 stage， BRAF^V600E mutation， and TERT promoter mutation （all P < 0.05）. Multivariate logistic regression analysis identified preoperative TSH level， tumor diameter > 0.5 cm， multifocal lesions， capsule invasion， extrathyroidal extension， T stage， BRAF^V600E mutation， and TERT promoter mutation as independent risk factors for OLNM in patients with PTMC （all P < 0.05）. ROC curve analysis demonstrated that the integrated model combining clinical pathological features?including tumor diameter， number of lesions， microcalcification， capsule invasion， extrathyroidal extension， and T stage?with genetic markers （BRAF^V600E and TERT promoter mutations） exhibited the highest predictive performance， yielding an AUC of 0.940. This was significantly higher than the model based solely on clinical pathological features （AUC = 0.736） or those relying exclusively on genetic testing （BRAF^V600E： AUC = 0.860； TERT： AUC = 0.882）， with all comparisons reaching statistical significance （P < 0.05）. Conclusions The integration of clinical pathological features with genetic testing significantly improved the predictive accuracy of OLNM in PTMC patients， surpassing models based solely on individual clinical pathological characteristics or genetic tests alone. This multimodal strategy offers a robust， evidence-based foundation for personalized surgical planning and enhances the precision of clinical decision-making in the management of PTMC.