<p>The rapid growth of Web3 trends has accelerated the exploration and adoption of decentralization, with blockchain technology providing enhanced security measures. In recent years, the ability of secure systems to detect and counter evolving attacks has been increasingly challenged as attackers devise novel strategies to gain the trust of users and devices. Through trust-related attacks, these malicious actors manipulate or introduce false ratings, artificially inflating the reputation of malicious nodes within the network. From this perspective, Trust Management Systems (TMS) play a pivotal role in identifying and neutralizing malicious entities. However, conventional TMS focus solely on trust assessment without considering the composition, updating, and propagation phases necessary to build a reliable and scalable system capable of supporting real-world applications. Although integrating blockchain into trust management systems strengthens distributed trust assessment through a decentralized architecture, it also introduces challenges related to scalability, deployment, and the cost of maintaining consensus protocols and smart contracts. In this survey, our objective is to examine the existing blockchain-based TMS, propose a new methodology, and highlight open issues and emerging challenges. Our study emphasizes the importance of integrating distributed TMS while ensuring coordination among trust phases. We underscore the potential of employing graph databases to enable efficient trust distribution and dynamic updates. Furthermore, we identify critical issues related to scalability, reliability, storage, computational costs, and real-time integration within Social Internet of Things (SIoT) environments. To address these challenges, we propose a methodology for upgrading conventional TMS to distributed systems based on blockchain technology.</p>