Artificial Intelligence (AI) is being integrated into Assisted Reproductive Technology (ART) to address challenges such as high costs, long procedures, and variable success rates. AI can optimize treatment protocols, improve embryo selection, and enhance patient counseling. This review explores AI's role in predictive modeling, image analysis, and personalized medicine, aiming to make ART more efficient, cost-effective, and tailored to individual needs. The integration of AI into ART involves several steps: data collection and preprocessing, algorithm development and training, validation and evaluation, clinical integration and deployment, and continuous monitoring and improvement. Key challenges include embryo selection accuracy, data privacy and security, ethical considerations, and integration into clinical workflows. AI-based systems must comply with regulations like GDPR and HIPAA to protect patient data. Ethical concerns include algorithmic bias and the need for transparent, fair AI algorithms. Successful AI implementation requires collaboration between multidisciplinary teams, including embryologists, clinicians, data scientists, and IT specialists. Post-deployment, continuous monitoring and feedback mechanisms are essential to ensure AI systems perform well in real-world settings. AI has the potential to revolutionize ART by improving accuracy, efficiency, and outcomes of fertility treatments. However, addressing concerns related to data privacy, ethics, and clinical integration is crucial for responsible AI implementation in fertility care. By following systematic steps in algorithm development, validation, and clinical integration, AI can empower healthcare providers to make more informed decisions and improve patient outcomes in reproductive medicine.Artificial Intelligence (AI) is being integrated into Assisted Reproductive Technology (ART) to address challenges such as high costs, long procedures, and variable success rates. AI can optimize treatment protocols, improve embryo selection, and enhance patient counseling. This review explores AI's role in predictive modeling, image analysis, and personalized medicine, aiming to make ART more efficient, cost-effective, and tailored to individual needs. The integration of AI into ART involves several steps: data collection and preprocessing, algorithm development and training, validation and evaluation, clinical integration and deployment, and continuous monitoring and improvement. Key challenges include embryo selection accuracy, data privacy and security, ethical considerations, and integration into clinical workflows. AI-based systems must comply with regulations like GDPR and HIPAA to protect patient data. Ethical concerns include algorithmic bias and the need for transparent, fair AI algorithms. Successful AI implementation requires collaboration between multidisciplinary teams, including embryologists, clinicians, data scientists, and IT specialists. Post-deployment, continuous monitoring and feedback mechanisms are essential to ensure AI systems perform well in real-world settings. AI has the potential to revolutionize ART by improving accuracy, efficiency, and outcomes of fertility treatments. However, addressing concerns related to data privacy, ethics, and clinical integration is crucial for responsible AI implementation in fertility care. By following systematic steps in algorithm development, validation, and clinical integration, AI can empower healthcare providers to make more informed decisions and improve patient outcomes in reproductive medicine.