ObjectiveTo summarize the current research progress of imaging in evaluating the efficacy of neoadjuvant therapy for breast cancer, analyze the clinical application value and limitations of conventional imaging modalities, and explore the application prospects of advanced technologies such as multimodal fusion, radiomics, and artificial intelligence in efficacy prediction. MethodBy searching recent domestic and international literature on imaging evaluation of neoadjuvant therapy for breast cancer, this review summarizes traditional imaging evaluation methods and the applications of cutting-edge technologies such as multimodal fusion, radiomics, and deep learning. ResultsTraditional imaging methods play an important role in the evaluation of post-treatment efficacy, but there are inherent limitations. The morphology-based evaluation paradigm struggles to meet the clinical demand for early prediction and exhibits a significant gap compared with the histopathological gold standard. In recent years, the development of multimodal fusion, radiomics, and deep learning technologies has demonstrated promising efficacy in predicting pathologic complete response. However, most studies are single-center retrospective designs, and their external validation and interpretability still need to be strengthened. ConclusionsImaging enables both retrospective assessment and prospective prediction in evaluating the efficacy of neoadjuvant therapy for breast cancer. In the future, the establishment of standardized imaging acquisition and validation databases, the development of hybrid models that combine predictive performance with interpretability, and the promotion of the deep integration of imaging with multi-omics data will be important directions for clinical transformation.