Identifying embryos with the greatest likelihood of successful implantation is a critical process of the in vitro fertilization (IVF) process. Traditional visual assessments, however, are constrained by the subjectivity of embryologists, making consistent evaluation of embryo health challenging. In recent years, advancements in artificial intelligence (AI) have introduced new methods for embryo health assessment, including computer vision and deep learning techniques that can automatically analyze embryo morphology images and extract key features. These approaches reduce subjectivity in decision-making and enhance efficiency. This review involved an extensive literature search, utilizing keywords like ”embryo health assessment” to focus on AI-driven approaches for automated evaluation. We highlight AI techniques applied to assessing embryos throughout early development, blastocyst, and full developmental stages. Our findings indicate that AI technologies can significantly enhance the precision, consistency, and speed of embryo selection processes. Compared to manual evaluations, AI demonstrates superior performance in several respects, offering new opportunities for advancing success rates and efficiency in reproductive medicine. We also examine the challenges AI faces in clinical practice and explore potential future directions. This review provides insights into AI’s contribution to advancing embryo selection and paves the way for the development of fully automated evaluation systems.