Objective: Post-traumatic epilepsy (PTE) is a common complication of traumatic brain injury (TBI) that significantly impacts the prognosis. Early prediction of PTE risk is crucialfor clinical management. Compared to adults, research on pediatric PTE remains limited, and there is currently no widely accepted high-performance predictive model for children with TBI.. This study aimed to develop and validate a nomogram prediction model for PTE risk in pediatric TBI patients. Methods:?We systematically searched the China National Knowledge Infrastructure (CNKI), Wanfang, Chinese Biomedical Literature Database (CBM), VIP, PubMed, Embase, and Web of Science for studies on risk factors of pediatric PTE, with a search timeframe from database inception to October 2024. Meta-analysis was performed using Stata 15.0 to identify risk factors with statistically significant pooled effect sizes. A retrospective cohort of 262 TBI children admitted to the SICU of Nanjing Medical University Affiliated Children's Hospital from January 2019 to December 2023 was included. The dataset was randomly split into a training cohort (70%) and an internal validation cohort (30%). Based on the risk factors identified in the Meta-analysis, a multivariate logistic regression model was constructed using R software, and a nomogram was developed. The model's discriminative ability was evaluated using the area under the receiver operating characteristic curve (AUC), calibration was assessed via the Hosmer-Lemeshow test, and clinical utility was examined using decision curve analysis (DCA). Results:?A total of 13 observational studies involving 1,371,819 TBI children were included. Meta-analysis revealed that the incidence of PTE in Chinese children with TBI was 19% (95% CI: 17%-20%). Based on the Meta-analysis findings and clinical expertise, the final prediction model incorporated eight key risk factors: Glasgow Coma Scale (GCS) score, open head injury, early seizure activity, loss of consciousness, and abnormal neuroimaging findings including intracranial hematoma, cerebral contusion, subdural hemorrhage, and subarachnoid hemorrhage. The model demonstrated strong discriminative ability, with AUCs of 0.801 (95% CI: 0.735–0.867, P < 0.05) in the training cohort and 0.831 (95% CI: 0.728–0.934, P < 0.05) in the validation cohort. The Hosmer-Lemeshow goodness-of-fit test indicated good calibration (training cohort: P = 0.079; validation cohort: P = 0.082). DCA confirmed substantial clinical net benefit. Conclusion:?The PTE risk prediction model developed in this study, based on Meta-analysis-derived risk factors, exhibits excellent discrimination, calibration, and clinical utility, serving as an effective tool for PTE risk assessment in children with TBI.