Neoadjuvant chemotherapy has become a standard treatment strategy for breast cancer， and magnetic resonance imaging （MRI） is the preferred imaging method for assessing the response of breast cancer to neoadjuvant chemotherapy. Although MRI can provide detailed information of tumor， including location， size， and microenvironment， the precise assessment of neoadjuvant chemotherapy of breast cancer suffers from the diverse changes in tumors present in MRI images. Artificial intelligence methods based on machine learning and deep learning have demonstrated the ability to recognize complex patterns in MRI data. Through clinical radiologic feature analysis， radiomics analysis， and habitat analysis， artificial intelligence technology has significantly enhanced the performance and efficiency of assessments for breast cancer neoadjuvant chemotherapy， aiding in the realization of personalized treatment strategies. This paper introduces the MRI data and performance indicators in assessing breast cancer neoadjuvant chemotherapy， summarizes the progress of artificial intelligence applications in this field， and discusses the current challenges and potential future research directions for artificial intelligence technology in practical applications.