Autonomous trust management for blockchain technology research investigates mechanisms that enable decentralized systems to evaluate, establish, and maintain trust among participants without relying on central authorities. Research papers in this domain focus on algorithms and frameworks that quantify trust based on factors such as transaction history, behavior patterns, reputation scores, and compliance with protocol rules. Studies explore applications in blockchain-based finance, IoT, supply chains, and smart contracts, where autonomous trust mechanisms help prevent fraud, malicious behavior, and collusion. Machine learning and reputation-based models are often integrated with consensus protocols to dynamically adjust trust ratings and decision-making in real time. Challenges addressed include scalability, resistance to Sybil or collusion attacks, latency in trust computation, and interoperability across heterogeneous blockchain networks. Overall, this research emphasizes designing self-regulating blockchain systems where trust is continuously assessed and enforced autonomously, enhancing security, reliability, and stakeholder confidence in decentralized environments.