Background: Previous studies have demonstrated that quantitative ultrasound (QUS) is an effective tool for monitoring breast cancer patients undergoing neoadjuvant chemotherapy (NAC). Here, for the first time, we demonstrate the clinical utility of pretreatment QUS texture features in predicting the response of breast cancer patients to NAC. Method: Using a 6 MHz center frequency clinical ultrasound imaging system, radiofrequency (RF) breast ultrasound data were acquired from 100 locally advanced breast cancer (LABC) patients prior to their NAC treatment. QUS spectral parameters including mid-band fit (MBF), spectral slope (SS), and spectral intercept (SI), and backscatter coefficient parameters including average acoustic concentration (AAC) and average scatterer diameter (ASD) were computed from regions of interest (ROI) in the tumor core and its margin. Subsequently, employing gray-level co-occurrence matrices (GLCM); textural features including contrast (CON), correlation (COR), energy (ENE), and homogeneity (HOM); and image quality features including core-to-margin ratio (CMR) and core-to-margin contrast ratio (CMCR) were extracted from the parametric images as potential predictive indicators. QUS results were compared with the clinical and pathological response of each patient determined at the end of their NAC. Results and Discussion: Results from the 100 patients indicate that a combined QUS feature model demonstrated a favorable RECIST-based (Response Evaluation Criteria in Solid Tumours) response (sensitivity = 83%, specificity = 79%, and AUC = 82%) and Miller-Payne-based response (sensitivity = 88%, specificity = 71%, and AUC = 83%), and were linked to patient survival (sensitivity = 71%, specificity = 92%, and AUC = 82%) predictions. Best results were obtained using a radial-basis function-support vector machine (RBF-SVM) learning algorithm. Only four features were selected in each binary response group classification. Conclusion: The findings of this study suggest that QUS features of a breast tumor are strongly linked to tumor responsiveness. The ability to identify patients who would not benefit from NAC would facilitate salvage therapy and a clinical management that has minimum patient toxicity and maximum outcome (and a better quantity/quality of life). Future work will include investigations into the ability of a QUS model in predicting patient survival on completion of chemotherapy and surgery, and the effect of including (i.e., estrogen/progesterone/human epidermal growth factor receptor 2 receptor status and histological grade) in the QUS-based predictive model.