To develop and validate machine learning models for HER2-zero and -low using MRI features pre-neoadjuvant therapy (pre-NAT). 516 breast cancer patients post-NAT surgery were randomly divided into training (n = 362) and internal validation sets (n = 154) for model building and evaluation. MRI features (tumor diameter, enhancement type, background parenchymal enhancement, enhancement pattern, percentage of enhancement, signal enhancement ratio, breast edema, and ADC) were reviewed. Logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), and extreme gradient boosting (XGBoost) models utilized MRI characteristics for HER2 status assessment in training and validation datasets. The best-performing model generated a HER2 score, subsequently correlated with pathological complete response (pCR) and disease-free survival (DFS). The XGBoost model outperformed LR, SVM, and KNN, achieving an area under the ROC curve (AUC) of 0.783(95% CI: 0.733-0.833) and 0.787(95% CI: 0.709-0.865) in the validation dataset. Its HER2 score for predicting pCR had an AUC of 0.708 in the training datasets and 0.695 in the validation dataset. Additionally, the low HER2 score was significantly associated with shorter DFS in the validation dataset (HR: 2.748,95% CI: 1.016-7.432, P = 0.037). The XGBoost model could help distinguish HER2-zero and HER2-low breast cancers, and has the potential to predict pCR and prognosis in breast cancer patients undergoing NAT. Human epidermal growth factor receptor 2 (HER2)-low-expressing breast cancer can benefit from the HER2 targeted therapy. Prediction of HER2-low expression is crucial for appropriate management. MRI features offer a solution to this clinical issue.