The integration of artificial intelligence (AI) into healthcare frameworks in the United States is transforming both operational efficiency and administrative effectiveness. As healthcare systems face increasing challenges from rising patient demands, resource constraints, and complex regulatory requirements, AI-driven solutions have emerged as a critical tool for modernization. This paper explores the evolving role of AI across key areas of healthcare operations and administration, offering insights into its transformative potential. From operational perspectives, AI is enhancing clinical workflows, streamlining diagnostic processes, and enabling predictive analytics for patient care. Advanced machine learning algorithms analyse vast datasets to identify patterns, improving disease prediction and treatment personalization. In administrative domains, AI automates repetitive tasks such as billing, coding, and appointment scheduling, reducing errors and improving resource allocation. Tools like natural language processing (NLP) are further optimizing medical documentation and facilitating communication between providers and patients. Despite its potential, the implementation of AI in healthcare faces barriers, including data privacy concerns, ethical considerations, and the need for robust infrastructure. This paper highlights strategies to address these challenges, emphasizing the importance of stakeholder collaboration, regulatory adaptation, and continuous workforce training. The paper concludes with a forward-looking analysis of how AI integration can contribute to a more efficient, patient-centered healthcare system, fostering innovation while addressing the complexities of modern healthcare delivery in the US. By leveraging AI technologies, healthcare frameworks can achieve enhanced operational resilience and deliver improved outcomes for patients and providers alike.