Background and Aims Postoperative pancreatic fistula (POPF) is one of the most severe and common complications following pancreaticoduodenectomy (PD) and is a major cause of mortality after PD. Given the multiple risk factors associated with PD-POPF, developing an effective predictive model is of significant clinical importance. This study was conducted to explore the predictive performance of the triglyceride-glucose (TyG) index combined with serological indicators for POPF following PD.Methods The preoperative general data, laboratory indicators within one week before surgery, and postoperative complication data of 291 patients who underwent PD at the Department of General Surgery, Second Hospital of Lanzhou University, from January 2019 to June 2024, were retrospectively collected. Patients were randomly divided into a modeling group (203 cases) and a validation group (88 cases) using a computer-generated random number method at a 7∶3 ratio. Univariate Logistic regression and multivariate binary Logistic regression (Back-Wald method) were performed on the modeling group data. Based on regression analysis results, a predictive model was constructed and visualized using a nomogram. The discriminative ability of the nomogram model was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). A calibration curve was used to assess the agreement between predicted and actual probabilities, and a decision curve analysis was conducted to evaluate the clinical application value of the model. Subgroup analysis was performed on potential factors influencing the outcome variables.Results Among the 291 patients, 70 developed POPF, with 49 cases in the modeling group and 21 in the validation group. There was no statistically significant difference between the two groups (all P>0.05). Univariate analysis in the modeling group identified body mass index (BMI), triglycerides, TyG index, albumin (ALB), platelet count (PLT), absolute lymphocyte count (LYM), and absolute neutrophil count (NEUT) as significant factors associated with POPF (all P<0.05). Multivariate analysis revealed that BMI, TyG index, ALB, PLT, LYM, and NEUT were independent influencing factors for POPF (all P<0.05). A PD-POPF risk prediction model and nomogram were constructed based on these results. The model achieved an AUC of 0.80 (0.73-0.86), and when applied to the validation group, the ROC analysis yielded an AUC of 0.80 (0.70-0.90). The calibration curves of both the modeling and validation groups closely aligned with the standard curve. Subgroup analysis indicated that tumor nature and tumor stage had minimal impact on PD-POPF risk factors, demonstrating good model stability.Conclusion The TyG index, along with BMI, PLT, NEUT, ALB, and LYM, is closely associated with PD-POPF occurrence. The risk prediction model based on the TyG index and these influencing factors exhibits good predictive performance and holds significant clinical value for guiding early intervention.