Healthcare is rapidly moving toward utilizing technologies that enhance the delivery, management, and security of healthcare. Telehealth has greatly streamlined access to medical care, especially in managing chronic diseases. The rapid development of IoT devices and digital health records posed critical challenges in data security and privacy, among other operational issues. A centralized system is vulnerable to breaches, and therefore, there is a need for more robust and decentralized solutions. There are risks involved in the current centralized system and a new stronger decentralized system is required. The blockchain has a way of providing safe, immutable ledgers with which health data can be managed; however, its static security measures need to be improved in the context of the kind of risks and at- tacks that may appear these days. This research looks to introduce Large Language Models into blockchain-based telehealth systems. LLMs enhance blockchain security through real- time detection of threats, anomalous behavior, and optimized execution of smart contracts as LLMs contain advanced processing and pattern recognition. This system combines both the merits of blockchain’s decentralized architecture and the intelligent analytics of LLMs to bring a dynamic and scalable framework of telehealth security, which enhances data privacy, operational transparency, and system resilience.