The increasing complexity and scale of distributed communication systems have intensified the need for security mechanisms that are both adaptive to evolving cyber threats and capable of ensuring high-integrity trust management. Conventional security approaches relying solely on centralized machine learning or static trust models struggle to maintain robustness, transparency, and resilience under dynamic and adversarial conditions. This paper proposes an integrated framework that combines adaptive AI-driven threat intelligence with blockchain-assisted trust management to enable secure and high-integrity communication systems. The proposed architecture employs deep learning-based adaptive models to analyze heterogeneous network and system data and generate probabilistic threat assessments in real time. These threat intelligence outputs are subsequently incorporated into a permissioned blockchain layer, where decentralized trust evaluation and smart contract–based policy enforcement ensure tamper-resistant and auditable security decisions. Performance evaluation using benchmark intrusion detection datasets and simulated network environments demonstrates that the proposed framework achieves higher threat detection accuracy, improved trust consistency under adversarial conditions, and acceptable communication latency compared with AI-only and blockchain-only baselines. The results indicate that tightly integrating adaptive intelligence and decentralized trust provides a practical and scalable solution for securing heterogeneous communication environments, including IoT, edge computing, and enterprise networks.