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Artificial Intelligence (AI) is rapidly transforming the healthcare sector by enabling intelligent systems to support diagnosis, clinical decision-making, disease prediction, treatment planning, patient monitoring, and healthcare management. This review examines the major applications of AI, including machine learning, deep learning, natural language processing, computer vision, generative AI, and predictive analytics across healthcare domains such as medical imaging, disease diagnosis, personalized medicine, drug discovery, clinical risk assessment, robotic surgery, and electronic health record analysis. AI-based approaches can improve diagnostic accuracy, facilitate early disease detection, optimize clinical workflows, and support data-driven personalized care. However, the widespread adoption of AI in healthcare is constrained by challenges related to data quality and availability, patient privacy, algorithmic bias, lack of interpretability, cybersecurity, interoperability, ethical concerns, regulatory uncertainty, and limited real-world validation. Future directions include the development of explainable and trustworthy AI, privacy-preserving learning, multimodal AI, generative AI, robust clinical validation, standardized regulatory frameworks, and human-centered AI systems. The review emphasizes that successful healthcare AI should augment rather than replace healthcare professionals, ensuring patient safety, transparency, accountability, equity, and clinical effectiveness. Responsible integration of AI with healthcare practices has the potential to improve healthcare outcomes while creating more efficient, accessible, and personalized healthcare systems.