Artificial intelligence (AI) has the potential to personalize and optimize assisted reproductive technology (ART) by analyzing large, dynamic datasets generated during ART cycles. This review discusses how AI can improve key steps in ART, including drug selection and dosing, cycle monitoring, oocyte maturation, and gamete/embryo selection, leading to better clinical outcomes. AI methods, such as machine learning (ML), robotics, and computer vision, can process complex data and provide personalized recommendations. Supervised learning methods like decision trees, random forests, and neural networks are used to predict outcomes, while unsupervised methods like k-means clustering and generative adversarial networks (GANs) help interpret data. Reinforcement learning is also applied to optimize decision-making. AI has been used to personalize gonadotropin dosing, optimize trigger days for oocyte maturation, and improve embryo selection. In the embryology lab, AI can analyze sperm morphology, motility, and DNA fragmentation to enhance sperm selection for intra-cytoplasmic sperm injection (ICSI). AI-based tools like VIOLET™ and MAGENTA™ predict oocyte and embryo quality based on morphological features. Additionally, AI can assess embryo development through morphokinetics and non-invasive aneuploidy testing. Omics-based approaches, including metabolomic signatures, are being explored to improve embryo prognosis. Despite these advancements, challenges remain in validating AI models, ensuring transparency, and integrating them into clinical practice. Future research should focus on large-scale, multi-center studies to establish the efficacy and reliability of AI in ART. The integration of AI into ART has the potential to improve clinical outcomes, reduce costs, and enhance personalized care. However, ethical, regulatory, and practical considerations must be addressed to ensure the safe and effective implementation of AI in reproductive medicine.Artificial intelligence (AI) has the potential to personalize and optimize assisted reproductive technology (ART) by analyzing large, dynamic datasets generated during ART cycles. This review discusses how AI can improve key steps in ART, including drug selection and dosing, cycle monitoring, oocyte maturation, and gamete/embryo selection, leading to better clinical outcomes. AI methods, such as machine learning (ML), robotics, and computer vision, can process complex data and provide personalized recommendations. Supervised learning methods like decision trees, random forests, and neural networks are used to predict outcomes, while unsupervised methods like k-means clustering and generative adversarial networks (GANs) help interpret data. Reinforcement learning is also applied to optimize decision-making. AI has been used to personalize gonadotropin dosing, optimize trigger days for oocyte maturation, and improve embryo selection. In the embryology lab, AI can analyze sperm morphology, motility, and DNA fragmentation to enhance sperm selection for intra-cytoplasmic sperm injection (ICSI). AI-based tools like VIOLET™ and MAGENTA™ predict oocyte and embryo quality based on morphological features. Additionally, AI can assess embryo development through morphokinetics and non-invasive aneuploidy testing. Omics-based approaches, including metabolomic signatures, are being explored to improve embryo prognosis. Despite these advancements, challenges remain in validating AI models, ensuring transparency, and integrating them into clinical practice. Future research should focus on large-scale, multi-center studies to establish the efficacy and reliability of AI in ART. The integration of AI into ART has the potential to improve clinical outcomes, reduce costs, and enhance personalized care. However, ethical, regulatory, and practical considerations must be addressed to ensure the safe and effective implementation of AI in reproductive medicine.