Artificial Intelligence (AI) represents a paradigm shift in healthcare, promising to enhance diagnostic accuracy, optimize treatment planning, streamline hospital operations, and accelerate drug discovery. The integration of AI in healthcare ranges from predictive analytics and clinical decision support systems to robotic surgery and personalized medicine. While the opportunities are significant, AI adoption is fraught with challenges including data privacy issues, algorithmic bias, lack of interpretability, regulatory gaps, and workforce readiness. Ethical implications surrounding autonomy, equity, transparency, and accountability further complicate deployment. This review synthesizes literature from diverse domains to provide a detailed examination of AI in healthcare, including empirical studies, case analyses, and ethical frameworks. Tables and graphs illustrate comparative opportunities and challenges, adoption trajectories, and risk assessment. Finally, the article proposes recommendations for research, policy, and clinical integration to ensure responsible, equitable, and effective AI adoption.