Objective To explore the risk of post-periprosthetic infection and construct a nomogram model for predicting the risk of peri-prosthetic infection after mammoplasty through studying serum interleukin-6 (IL-6) and toll-like receptor 2 (TLR-2) levels as well as clinical data of patients who underwent mammoplasty. Methods Patients who underwent mammoplasty in a hospital from February 2019 to February 2022 were selected as research subjects and divided into the infected and non-infected groups. Univariate analysis was performed to analyze patients' general condition, clinical parameters, as well as serum IL-6 and TLR-2 levels, Risk factors affecting the occurrence of peri-prosthetic infection after mammoplasty were preliminarily screened. Independent risk factors were further screened with multivariate logistic regression analysis. A nomogram model for predicting the risk of peri-prosthetic infection after mammoplasty was constructed and validated. Results A total of 446 patients who underwent mammoplasty were included in the analysis, with an average age of (28.50±3.39) years old. Forty-two cases (9.42%) were devided into the infected group and 404 cases (90.58%) in the non-infected group. The levels of IL-6 and TLR-2 in the infected group were significantly higher than those in the non-infected group, with statistically significant difference (P < 0.05). Independent risk factors, including the history of diabetes and mastitis, C-reactive protein (CRP), white blood cell (WBC), as well as IL-6 and TLR-2 levels, were independent risk factors for post-periprosthetic infection after mammoplasty. Based on the screened risk factors, a nomogram model was constructed. The area under the curve (AUC) of receiver operating characteristic (ROC) curve before and after internal validation were 0.858 (95%CI: 0.804-0.911) and 0.842 (95%CI: 0.799-0.890), respectively. The cut-off value was 0.56. The sensitivity before and after internal validation were 88.1% and 88.2%, respectively, and the specificity were 92.3% and 91.7%, respectively, indicating good accuracy. Conclusion The history of diabetes and mastitis, CRP, WBC, IL-6 and TLR-2 levels have a good predictive ability for peri-prosthetic infection after mammoplasty. The nomogram model constructed based on these risk factors can predict the risk of peri-prosthetic infection after mammoplasty, which can assist clinicians to develop individualized treatment plans for patients and reduce the risk of post-operative infection.