Objective To develop and validate a predictive model for neoadjuvant chemotherapy response in locally advanced breast cancer based on magnetic resonance imaging (MRI) parameters and multimodal ultrasound.Methods A retrospective analysis was conducted on data from 238 patients with locally advanced breast cancer who were treated at the 940 Hospital of the Joint Service Support Force of the Chinese People's Liberation Army from January 2019 to May 2023. They were randomly divided into the training set (167 cases) and the internal validation set (71 cases). Another retrospective analysis was conducted on data from 66 patients with locally advanced breast cancer who were treated at the same hospital from June 2023 to June 2024 for external validation. Patients underwent surgical resections after neoadjuvant chemotherapy, and the pathological complete response (pCR) was analyzed. MRI and multimodal ultrasound examinations were performed before and after neoadjuvant chemotherapy to analyze factors influencing the therapeutic response in patients with locally advanced breast cancer. Based on MRI parameters and multimodal ultrasound features, a predictive model for neoadjuvant chemotherapy efficacy was developed and validated.Results Thirty-nine cases (23.35%) reached pCR in the training set, and 16 (22.54%) and 16 cases (24.24%) reached pCR in the internal validation set and the external validation set, respectively. The proportion of stage III tumors, the change in PI (ΔPI), post-chemotherapy TTP, change in TTP (ΔTTP), post-chemotherapy ADC, and change in ADC (ΔADC) were all significantly higher in the pCR group than in the non-pCR group (P < 0.05), whereas the post-chemotherapy PI was significantly lower in the pCR group (P < 0.05). Multivariable stepwise Logistic regression analysis revealed that stage III tumors [O^R = 4.627 (95% CI: 1.582, 13.538) ], high ΔADC [O^R = 4.371 (95% CI: 1.494, 12.788) ], and high ΔPI [O^R = 3.785 (95% CI: 1.294, 11.073) ] were all risk factors affecting the effect of neoadjuvant chemotherapy in locally advanced breast cancer (P < 0.05). Influencing factors were used as predictor variables to establish a nomogram prediction model, with the predicted risk ranging from 0.08 to 0.56. The calibration curve of the nomogram model for predicting the efficacy of neoadjuvant chemotherapy in patients with locally advanced breast cancer closely approximated the ideal curve (P > 0.05). In the training set, the ROC curve analysis showed that the nomogram had a sensitivity of 79.94% (95% CI: 0.673, 0.887), a specificity of 78.66% (95% CI: 0.676, 0.873), and an area under the curve (AUC) of 0.823 (95% CI: 0.751, 0.913). In the internal and external validation sets, the model demonstrated sensitivities of 82.67% (95% CI: 0.711, 0.917) and 79.13% (95% CI: 0.681, 0.878), specificities of 79.25% (95% CI: 0.682, 0.879) and 83.19% (95% CI: 0.715, 0.923), and AUCs of 0.874 (95% CI: 0.779, 0.983) and 0.867 (95% CI: 0.754, 0.962), respectively, indicating good diagnostic performance of the model.Conclusion Tumor stage, change in ADC, and change in PI are associated with the efficacy of neoadjuvant chemotherapy in patients with locally advanced breast cancer. The predictive model for treatment response constructed based on these factors demonstrates good performance.