Successful embryo implantation requires high-quality embryos and endometrium with high receptivity. However, there is no universally recognized authoritative technique for evaluating endometrial receptivity (ER) at present. In recent years, the application of artificial intelligence (AI) technology in the assessment of ER has achieved remarkable progress. In ultrasound image analysis, AI has improved the accuracy of endometrial segmentation and measurement through deep learning technology, and can combine clinical data for pregnancy outcome prediction. In histological analysis, AI algorithms can quickly and accurately identify histological features of the endometrium and analyze the ratio of epithelial and stromal cells. In genomic diagnosis, AI analyzes endometrial gene expression through machine learning methods to provide scientific basis for personalized embryo transfer. Although AI shows great potential in the assessment of ER, its application still faces challenges such as data standardization and algorithm generalization ability. In the future, through multi-center data sharing and algorithm optimization, AI is expected to play a greater role in assisted reproductive technology. This article reviews the application of AI in ER assessment, summarizes the latest research progress, and provides references for further research.