Artificial intelligence (AI) is rapidly reshaping embryo culture in assisted reproductive technology (ART), challenging the traditional reliance on embryologists’ subjective expertise. This study provides a comprehensive literature-based review of AI implementation across key embryology processes, including oocyte assessment, sperm selection, fertilization evaluation, embryo grading, embryo transfer selection, micromanipulation, and laboratory quality management, based on PubMed-indexed publications from 2000 to 2026. The findings reveal that AI, particularly deep learning–driven image analysis, can achieve performance comparable to or exceeding that of experienced embryologists, improving objectivity, reproducibility, and workflow efficiency. Notably, AI-assisted embryo selection shows promise in enhancing clinical outcomes and standardizing decision-making; however, its reliability remains constrained by model instability, data heterogeneity, limited prospective validation, and ethical concerns such as algorithmic bias and lack of transparency. To address these emerging demands, we propose a two-stage educational framework for embryologist training: the development of “AI users,” equipped with foundational AI literacy and critical appraisal skills, followed by “AI supervisors,” capable of integrating AI into clinical decision-making, quality management, and ethical governance. As AI becomes integral to ART practice, the establishment of structured, standardized curricula is urgently required to ensure safe, effective, and responsible implementation.