Background This study collected and analyzed clinical data from patients with psoriasis, developing and validating a risk prediction model for psoriasis relapse. The aim is to improve the efficiency and accuracy of early screening for psoriasis relapse in clinical practice and to provide a reference for implementing preventive measures. Objective To develop and validate a risk prediction model for psoriasis relapse. Methods A convenience sampling method was used to select 504 psoriasis patients admitted to a tertiary hospital in China between January 2022 and December 2024, including 353 cases in the training set and 151 cases in the test set. Independent risk factors for psoriasis relapse were identified through univariate analysis and logistic regression analysis to develop a prediction model. A nomogram and SHAP summary plot were generated for model visualization, and the model’s goodness of fit and discriminative ability were evaluated. Results The one-year relapse rate of psoriasis patients after treatment was 66.67%. Logistic regression analysis identified body mass index (BMI), diabetes, biologic agent use, smoking, upper respiratory tract infection (URTI), and non-standard medication as independent risk factors for psoriasis relapse, which were included in the model. The area under the ROC curve (AUC) values for the training and testing sets were 0.767 [95% CI: 0.715–0.818] and 0.704 [95% CI: 0.620–0.789], respectively. The model demonstrated good discrimination and calibration, and decision curve analysis (DCA) showed significant net benefit in both the training and testing sets. Conclusion The psoriasis relapse risk prediction model developed in this study demonstrated good predictive performance. This model can serve as an effective reference for assessing the risk of psoriasis relapse and provides valuable insights for developing personalized prevention strategies for patients.