OBJECTIVE: To identify independent predictive factors for the presence of euploid blastocysts during the preimplantation genetic testing for aneuploidy (PGT-A) cycle and to develop a clinical predictive model. METHODS: A retrospective analysis was conducted on the clinical data from 501 treatment cycles of PGT-A performed at the Department of Reproductive Medicine, Northern Theater Command General Hospital, between June 2022 and June 2025. Patients were divided into an acquisition group (n = 447) and a non-acquisition group (n = 54) based on whether euploid blastocysts were obtained. Independent predictive factors were identified using uni-variate and multivariate Logistic regression analyses; model fit and discrimination were evaluated using the Hosmer-Lemeshow test and ROC curves; internal validation was performed using the Bootstrap method (1 000 bootstrap samples); and a nomogram was constructed for clinical visualization. RESULTS: Multivariate Logistic regression analysis identified maternal age (OR = 0.872, P = 0.025), maternal anti-Müllerian hormone (AMH) level (OR = 1.514, P = 0.001), and number of PGT cycles (per additional cycle: OR = 0.392, P < 0.001) as independent predictors of obtaining euploid blastocysts. Sensitivity analysis revealed that the positive association between paternal BMI (OR = 1.116, P = 0.031) lost statistical significance after excluding extreme values (OR = 1.034, P = 0.618), and no nonlinear relationship was observed (square term P = 0.189), therefore paternal BMI was not included in the final model. The area under the curve (AUC) for the simplified model (including maternal age, AMH level, and number of PGT cycles) was 0.828 (95%CI: 0.755-0.865); the Hosmer-Lemeshow test yielded P = 0.852, and the calibrated slope after bootstrap adjustment was close to 1, indicating high model stability and low risk of overfitting. CONCLUSION: Advanced maternal age, decreased AMH levels, and an increased number of previous PGT cycles are independent risk factors for failure to obtain euploid blastocysts during a PGT-A cycle. The predictive model developed based on these three factors demonstrates good discrimination and calibration capabilities; the nomogram can serve as a reference for clinical personalized counseling.