This paper investigates the transformative role of Artificial Intelligence (AI) in modern healthcare systems, emphasizing its impact on predictive, preventive, and personalized care delivery. It begins by defining key concepts, including intelligent healthcare systems and the critical pillars of predictive, preventive, and personalized care. The paper reviews the latest literature on AI applications in healthcare, exploring the integration of AI technologies with healthcare infrastructure, and discusses various AI techniques, including machine learning, deep learning, and big data analytics, that are employed to enhance healthcare outcomes. Theoretical and architectural frameworks supporting AI-driven healthcare are examined, and methodological approaches to implementation are discussed. Real-world case studies demonstrate the effectiveness of AI in improving early diagnosis, personalizing treatment, and reducing healthcare costs. Key challenges, including technical hurdles, ethical concerns, regulatory limitations, and data privacy issues, are addressed. The paper concludes with a discussion on future research directions, emphasizing the need for robust evidence, integration into clinical workflows, and scalability of AI systems to ensure equitable and sustainable healthcare solutions. The ultimate goal is to highlight how AI-enabled systems can transition healthcare from a reactive to a proactive, individualized approach to care, benefiting both patients and healthcare providers.