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This observational study was designed to establish and validate a stigma prediction model for patients with polycystic ovary syndrome (PCOS). The stigma risk scoring table for overweight and obese patients with PCOS has good predictive ability. When an overweight or obese patient with PCOS presents, the prediction model allows clinic staff to rapidly grade hirsutism, acne, and acanthosis, determine fertility desire, and quantify anxiety. Low-risk patients then receive standard care, whereas high-risk patients receive precision interventions. Unlike the traditional approach, this clinical prediction model incorporates not only laboratory values but also body-image concerns and psychological well-being, providing more comprehensive management for women with PCOS. To preliminarily explore the associations among stigma, PCOS signs, anxiety, and depression. A total of 124 overweight and obese patients with PCOS were selected using convenience sampling. The patients in the order of clinic visit time were divided into a modeling set and a validation set at a ratio of 3:1. Univariate analysis was first performed, normally distributed continuous variables were compared using t-tests, non-normally distributed continuous variables with nonparametric rank-sum tests, and categorical variables with χ2 tests. Independent risk factors for stigma were identified using multivariable logistic regression, and a nomogram was constructed. The model's discrimination and calibration were evaluated with the receiver operating characteristic curve and calibration curve. Internal validation was subsequently conducted on the validation data set to assess model performance comprehensively. Hirsutism (odds ratio [OR]=0.075, 95%Cl: 0.015-0.368) , acne (OR=0.210, 95%Cl: 0.050-0.878) , acanthosis nigricans (OR=0.184, 95%Cl: 0.044-0.073) , fertility requirements (OR=0.212, 95%Cl: 0.051-0.890) , and anxiety (OR=1.217, 95%Cl: 1.074-1.378) were independent influencing factors for stigma in these patients (P < .05). The constructed prediction model also demonstrated good predictive ability, with area under the curve values of 0.941 and 0.803 for the modeling and validation sets, respectively. Internal validation using 1000 bootstrap resamples revealed a mean area under the receiver operating characteristic curve area under the curve of 0.941.