This retrospective observational study aims to develop and validate a clinicopathology-based machine learning model to predict pathological complete response (pCR) following neoadjuvant chemotherapy in patients with breast cancer. Clinical and pathological data collected between 2010 and 2025 were used to train and evaluate multiple machine learning algorithms using cross-validation and independent holdout testing. The primary outcome was pathological complete response after neoadjuvant chemotherapy. Model performance was assessed using discrimination and classification metrics, including ROC-AUC, precision-recall AUC, F1-score, and Matthews correlation coefficient. The resulting model is intended to support clinical decision-making by providing individualized probability estimates of treatment response.