This advanced study introduces a highly enhanced Retrieval-Augmented Generation (RAG) in healthcare NLP systems designed to reform and revolutionize both forms of mental and physical healthcare delivery. Our system integrates the most advanced NLP techniques into RAG capabilities with very personalized, adaptive, and context-aware communication approaches in healthcare. Its design features the integration of multi-modal input processing, RAG-based information retrieval, core NLP functionalities, and ethic consideration as it defines possible ways of taking care of accurate and empathetic healthcare support. The system has a good 95.3% accuracy rate in diverse medical scenarios for both diagnostic suggestions and treatment and even personalized patient interactions. Some of its key features include long-term memory integration for better contextual retrieval, cross-language support, and user-specific conversational agents which adapt to and mirror the user's characteristics and emotional state. The NLP system, with RAG support, directly tackles major areas of concern within healthcare by creating non-judgmental, confidential, and easy access to the support needed and subsequently reducing barriers to healthcare seeking. Results are promising but require further refinement and clinical validation for practical application in the real world. This paper is a significant contribution to healthcare supported by AI systems, opening the door to much more intelligent, empathetic, and efficient health support.