Transforming healthcare with Natural Language Processing (NLP) is revolutionizing how medical data is analyzed, interpreted, and utilized. This paradigm shift is fueled by NLP's ability to process diverse healthcare data sources such as medical research articles, imaging reports, electronic health records, clinical notes, and patient feedback. Key NLP tasks like text summarization, information extraction, named entity recognition, sentiment analysis, and question answering are at the core of enabling advanced applications in healthcare. These include medical research acceleration, administrative task automation, clinical decision support systems (CDSS), and patient care enhancement. Despite its promise, challenges such as limited annotated datasets, biases in models, integration with legacy systems, and ensuring data privacy and security persist. The integration of NLP-driven solutions into healthcare systems has immense potential to enhance efficiency, improve patient outcomes, and optimize decision-making processes. Addressing these challenges through innovation and collaboration will ensure the sustainable growth of NLP in smart healthcare systems.