Background: Artificial Intelligence (AI) is rapidly transforming healthcare and medical education through the integration of machine learning, deep learning, natural language processing, and generative AI technologies. AI-powered educational tools have demonstrated considerable potential in enhancing teaching, learning, assessment, simulation-based training, and curriculum development. However, alongside these opportunities, concerns related to implementation challenges, ethical issues, academic integrity, and data privacy have emerged. Understanding the role of AI in medical education is essential for its responsible and effective integration into future healthcare training. Objective: To review the current evidence regarding the applications of Artificial Intelligence in medical education, with a focus on its opportunities, challenges, and ethical considerations. Methodology: A narrative review of the literature was conducted using electronic databases including PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, ERIC, and Cochrane Library, along with publications from international organizations. A total of 512 records were initially identified, of which 90 articles and guideline documents were finally included after screening and eligibility assessment. The selected literature was analyzed under major themes including AI applications in teaching and learning, assessment and evaluation, generative AI, implementation challenges, ethical issues, and future directions. Results: Among the 90 included studies, AI applications in teaching and learning constituted the largest thematic area (24.4%), followed by generative AI and large language models (22.2%) and ethical considerations (20.0%). Approximately 82.2% of studies reported improved learning outcomes with AI-assisted educational tools, while 80.0% highlighted the benefits of personalized learning. The utility of generative AI platforms such as ChatGPT was reported in 86.7% of studies. Ethical concerns related to academic integrity, plagiarism, and authorship were identified in 71.1% of studies, whereas 64.4% reported concerns regarding data privacy and confidentiality. Furthermore, 77.8% of studies emphasized the need for faculty training and AI literacy, and 83.3% recommended formal integration of AI competencies into medical curricula. Conclusion: Artificial Intelligence offers substantial opportunities to enhance medical education through personalized learning, intelligent assessment, simulation-based training, and educational support. Nevertheless, challenges related to reliability, accessibility, ethical use, academic integrity, and data security must be addressed. Responsible implementation of AI, supported by appropriate educational policies, faculty development, and ethical frameworks, is essential to maximize its benefits while minimizing potential risks. The integration of AI literacy into medical curricula will be crucial for preparing future healthcare professionals for technology-enabled healthcare environments.