Translation Memory (TM) systems are critical components of modern computer-aided translation (CAT) workflows. However, centralized TM platforms often lack transparency, user control, and verifiable trust guarantees. This paper introduces a decentralized architecture that enables scalable fuzzy search over TM segments while ensuring data provenance and integrity through blockchain validation. The proposed solution integrates Elasticsearch for high-performance approximate matching with Hyperledger Fabric as a trust-enforcing validation layer. The proposed system is designed to be interoperable with standard CAT tools via a backend gateway that performs fuzzy retrieval and verifies match authenticity using smart contracts. Importantly, only hashed metadata is stored on-chain, preserving confidentiality while enabling auditability. We conducted four experimental rounds with datasets of 100k, 500k, 1M and 10M segments to assess the system’s performance and scalability. Results show that the architecture maintains sub-second query times even at scale, with the blockchain validation layer introducing minimal overhead. These findings demonstrate the feasibility of integrating decentralized trust mechanisms into real-time linguistic data systems. The research illustrates how database engineering principles can be effectively combined with blockchain technologies to meet the evolving demands of secure and decentralized collaboration.