The study aimed to establish and validate a nomogram model to predict postoperative delirium (POD) among esophageal cancer resection patients. Clinical data of 396 patients with esophageal cancer who underwent esophagectomy from November 2020 to June 2023 in the electronic medical records of cardiothoracic Surgery, Affiliated Hospital of Jiangnan University. Participants were randomly divided into training and testing sets in a 7:3 ratio. Predictors were screened by Least absolute shrinkage and selection operator (LASSO) regression analysis and a nomogram model was built. The discrimination and consistency of the model were evaluated using the area under the receiver operating characteristic curve (AUC), C-statistic, Brier score, Hosmer-Lemeshow goodness-of-fit test, calibration curve and decision curve analysis (DCA). The results were validated using 1000 bootstraps resampling internal validation and testing set. Among 32 potential predictors, the final prediction model included 6 variables: postoperative pain, postoperative infection, dexmedetomidine use, propofol use, duration of mechanical ventilation, and Prognostic Nutritional Index (PNI). The model showed a good discrimination with an AUC of 0.919 (95% CI: 0.885- 0.953) in the training set, and adjusted to 0.911 (95% CI: 0.878- 0.944) and 0.871 (95% CI: 0.802- 0.940) in the internal validation and the testing set, respectively. ROC curves, calibration curves, DCA curves, C-statistic, Brier score and Hosmer-Lemeshow goodness-of-fit test showed excellent model performance. This study successfully established and validated the first POD prediction model for patients with esophageal cancer resection. It could accurately predict the occurrence of POD and effectively identify the high-risk patients, which is of great significance for improving the risk stratification of the population and for implementing targeted prevention intervention measures.