Background: Postoperative delirium (POD) is a common and serious neurological complication following acute type A aortic dissection (ATAAD) repair, contributing to increased morbidity and mortality. Given the critical role of systemic inflammation in POD pathogenesis, this study aimed to evaluate perioperative inflammatory profiles in patients undergoing ATAAD repair and to develop a predictive nomogram for early POD risk stratification. Methods: This retrospective, single-center study included 355 patients who underwent total arch replacement for ATAAD. Serial inflammatory markers were assessed across multiple perioperative time points. A two-stage variable selection process, incorporating variance inflation factor (VIF) and the Boruta algorithm, was utilized to identify robust predictors. A predictive nomogram was constructed via multivariable logistic regression. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA). Internal validation was performed using 1000 bootstrap resamples. Results: POD occurred in 29.86% of the cohort. Patients who developed POD had higher inflammatory marker levels at several individual postoperative time points compared to the non-delirium group. The final predictive model integrated 11 key variables: age, EuroSCORE I, CPB time, SIRI_6h, PLR_6h, IL-10_6h, IL-8_6h, IL-8_pre, IL-6_6h, IL-6_pre, and PCT_6h. The nomogram demonstrated acceptable internal discrimination with an AUC of 0.835 (95% CI: 0.794–0.876) and an optimism-corrected AUC of 0.811. Notably, IL-6_6h and PCT_6h emerged as strong independent risk factors, while SIRI_6h demonstrated an inverse association with POD. Calibration and DCA analyses confirmed the model’s acceptable performance and potential clinical utility, though independent external validation remains essential prior to any clinical translation. An exploratory, web-based risk calculator was developed to facilitate bedside implementation. Conclusions: This study develops and internally validates an exploratory biomarker-based prediction model for POD in ATAAD patients. By integrating early inflammatory profiles with established clinical variables, this preliminary model provides a preliminary foundation for early risk stratification, though rigorous prospective multicenter external validation is required before real-world clinical implementation can be considered.