Blockchain technology has attracted considerable attention in recent years due to its decentralized architecture, transparency, and inherent security features. Despite these advantages, Blockchain networks continue to face persistent challenges related to scalability, efficiency, and performance, particularly as transaction volumes and user demands increase. This study introduces an adaptive Artificial Intelligence (AI) driven framework designed to enhance the scalability and efficiency of Blockchain networks. By integrating AI algorithms capable of real-time learning and predictive optimization, the proposed model dynamically manages critical network functions such as transaction scheduling, resource allocation, and congestion control. The framework leverages both historical data and real-time analytics to make informed adjustments, thereby reducing latency, improving throughput, and optimizing energy consumption within Blockchain systems. The urgency of this research lies in addressing the scalability bottleneck that continues to hinder widespread Blockchain adoption across sectors such as finance, supply chain, healthcare, and human resource management. The novelty of this work resides in the fusion of adaptive AI techniques with Blockchain infrastructures, a combination that has been relatively underexplored in current scholarship. By advancing beyond static optimization methods, this research provides a more resilient and intelligent approach to Blockchain performance enhancement. The findings are expected to contribute to both aca- demic discourse and practical applications by offering a scalable, AI-empowered framework that can be adapted across multiple domains. Ultimately, this study aims to broaden the real-world applicability of Blockchain technology by overcoming its most pressing limitations.
Gustiah et al. (Fri,) studied this question.
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