The history of how artificial intelligence (AI) has been integrated into the Medicare process has been responsible and contingent, aiming to achieve personalized and effective healthcare. Core AI concepts, such as machine learning (ML), deep learning (DL), natural language processing (NLP), computer vision, and predictive analytics, have revolutionized conventional Medicare technologies. These advancements optimize disorder diagnosis, provide treatment plans tailored to individual cases, enhance medical imaging, and improve patient care. Current trends in AI adoption focus on disease detection, personalized treatment, remote access to healthcare, and they address major roadblocks such as data privacy, security, interoperability, mitigation of bias, regulatory compliance, and change resistance. Looking ahead, AI is poised to revolutionize not only drug discovery but also predictive analytics, promising the governance of ethical AI to further enhance healthcare accessibility and quality. To unlock the full potential of AI in Medicare services, ethical and regulatory considerations, including data privacy, transparency, mitigation of bias, and AI governance, must be carefully navigated. Additionally, stakeholders need to address change management, provide continuous education, and ensure a framework for ethical AI governance. These efforts will be essential for realizing the transformative benefits of AI in the realm of Medicare services.