Health monitoring systems built around IoT have a critical role in patient care in real-time, and guaranteeing data integrity, trust, and security across the distributed devices with limited resources is one of the chief issues. Traditional Practical Byzantine Fault Tolerance (PBFT) consensus systems are limited in scalability, latency, and susceptible to bad or malicious nodes, which is incompatible with medical IoT. To overcome these problems, this paper will introduce Trust-PBFT, which is a trust-based consensus algorithm targeting the needs of IoT-enabled healthcare networks. The proposed solution involves the incorporation of a decentralized trust evaluation model whereby every Internet of Things node has a dynamic trust rating that is based on its past behavior history and transaction history. Consensus decisions give preference to highly trusted nodes, which will minimize the possibility of evil participation and enhance fault tolerance. The model also uses lightweight methods of cryptography with adaptive measures of trust to facilitate secure data combining and effective decision-making. Through experimental evidence, one can state that Trust-PBFT is more scalable, secure, and efficient, as opposed to classical PBFT, which fits perfectly with equitable and trustworthy healthcare data handling during the presence of IoT applications.