The article reviews the potential of artificial intelligence (AI) to personalize and optimize assisted reproductive technology (ART). Infertility affects 1 in 6 couples, and many require repeated intensive cycles of ART to achieve a live birth. Traditional ART relies heavily on clinical experience and operator-dependent decisions, which can lead to inconsistent and reproducible outcomes. AI, with its ability to handle large, dynamic datasets, offers a promising solution. The review highlights how AI can improve key steps in ART, including drug selection and dosing, cycle monitoring, oocyte maturation induction, and embryo selection, to enhance overall efficacy and safety. Key AI methods discussed include machine learning (ML), robotics, and computer vision. ML techniques such as supervised, unsupervised, and reinforcement learning are used to develop models that optimize ART protocols. For example, ML models can personalize gonadotropin dosing, optimize oocyte maturation triggers, and predict oocyte quality based on morphological features. AI-driven tools have shown promising results in reducing the subjectivity and variability in ART, improving clinical outcomes, and reducing costs. The article also explores the application of AI in pre-treatment counseling, sperm assessment, oocyte assessment, and embryo selection. AI can provide more objective and accurate assessments, reducing the need for invasive procedures and improving patient outcomes. However, the authors emphasize the importance of rigorous validation, ethical considerations, and interdisciplinary collaboration to ensure the reliable and responsible use of AI in ART. In conclusion, the integration of AI into ART has the potential to revolutionize the field by improving personalized treatment, reducing resource consumption, and enhancing clinical outcomes. Further research and validation are needed to fully realize these benefits.The article reviews the potential of artificial intelligence (AI) to personalize and optimize assisted reproductive technology (ART). Infertility affects 1 in 6 couples, and many require repeated intensive cycles of ART to achieve a live birth. Traditional ART relies heavily on clinical experience and operator-dependent decisions, which can lead to inconsistent and reproducible outcomes. AI, with its ability to handle large, dynamic datasets, offers a promising solution. The review highlights how AI can improve key steps in ART, including drug selection and dosing, cycle monitoring, oocyte maturation induction, and embryo selection, to enhance overall efficacy and safety. Key AI methods discussed include machine learning (ML), robotics, and computer vision. ML techniques such as supervised, unsupervised, and reinforcement learning are used to develop models that optimize ART protocols. For example, ML models can personalize gonadotropin dosing, optimize oocyte maturation triggers, and predict oocyte quality based on morphological features. AI-driven tools have shown promising results in reducing the subjectivity and variability in ART, improving clinical outcomes, and reducing costs. The article also explores the application of AI in pre-treatment counseling, sperm assessment, oocyte assessment, and embryo selection. AI can provide more objective and accurate assessments, reducing the need for invasive procedures and improving patient outcomes. However, the authors emphasize the importance of rigorous validation, ethical considerations, and interdisciplinary collaboration to ensure the reliable and responsible use of AI in ART. In conclusion, the integration of AI into ART has the potential to revolutionize the field by improving personalized treatment, reducing resource consumption, and enhancing clinical outcomes. Further research and validation are needed to fully realize these benefits.