Background and Aims The risk factors for plasma cell mastitis (PCM) remain unclear. The disease is characterized by acute onset, rapid progression, prolonged course, lack of specific drugs, and high recurrence rates despite current clinical treatments using both Western and traditional Chinese medicine. Identifying and minimizing the risk factors for PCM to achieve early prevention has become a critical focus. This study investigated the risk factors associated with PCM and established a nomogram prediction model to estimate the probability of PCM occurrence in at-risk populations, providing guidance for disease prevention.Methods The clinical data from 82 PCM patients (study group) treated in the Breast Surgery Department of Hangzhou Obstetrics and Gynecology Hospital between January 2019 and January 2022 were retrospectively collected. Additionally, 82 middle-aged and young women who underwent health check-ups during the same period and had no breast diseases were randomly selected as the control group. Univariate and multivariate Logistic regression analyses were used to identify risk factors for PCM. A nomogram model predicting PCM risk was developed, and the receiver operating characteristic (ROC) curve, calibration curve, and concordance index (C-index) were used to evaluate its predictive performance.Results All patients in the study group had a history of abortion and/or childbirth, with a postpartum/post-abortion onset interval ranging from 1 to 7 years, averaging (3.37±1.91) years. Univariate analysis revealed significant differences between the two groups in body mass index, proportion of nipple retraction, number of pregnancies, history of trauma (including massage or lactation promotion), and lipid levels (all P<0.05). Multivariate Logistic regression analysis identified nipple retraction (OR=0.074, 95% CI=0.023-0.239, P=0.000), ≥3 pregnancies (OR=0.047, 95% CI=0.008-0.288, P=0.001), and history of trauma (OR=0.153, 95% CI=0.059-0.399, P=0.000) as independent risk factors for PCM. The nomogram model constructed based on these factors demonstrated a C-index of 0.855, indicating moderate accuracy. The calibration curve, generated using the "Boot" method, showed good agreement with the ideal curve. The area under the ROC curve was 0.855 (95% CI=0.800-0.910).Conclusion Nipple retraction, history of trauma, and the number of pregnancies are closely associated with PCM occurrence. The established nomogram prediction model exhibits moderate accuracy and good performance. It can be used clinically to predict the risk of PCM in women during high-incidence periods. It provides a quantitative estimation of disease probability, which can serve as a basis for offering targeted prevention recommendations to high-risk individuals.