AI-Powered Interoperable Blockchain Framework Using Deep Learning and Lightweight Consensus for Enhanced Security and Scalability

Abstract

Blockchain technology has emerged as a disruptive paradigm for secure and transparent data exchange; however, it continues to face significant challenges in scalability, interoperability, and security. Fragmentation across blockchain networks restricts seamless data integration, while traditional consensus mechanisms such as Proof of Work and Proof of Stake impose high computational costs and latency. To address these limitations, this article proposes an Intractive Blockchain structure to AI that takes advantage of deep learning and light consensus mechanisms to improve performance and safety. The proposed structure introduces three main contributions: (i) a model of detection of deep learning vulnerabilities that identifies real-time intelligent contract weaknesses to reduce application failures; (ii) a lightweight consensus protocol inspired by Byzantine failure tolerance (BFT) to minimize latency and improve the transfer rate, ensuring safe authentication; and (iii) a cross -chain interoperability layer that facilitates the perfect data exchange between heterogeneous blockchain networks. Experimental assessment of TensorFlow Hyperledger tissue shows that the proposed model improves the accuracy of vulnerabilities detection by up to 96 %, reaches a 23 % reduction in latency and increases the transfer rate by 18 % compared to conventional approaches. This research highlights the potential of AI-Empowered blockchain systems for scalable, secure and interpreter applications in financial, health and public services.