Scalability Enhancement for Blockchains by Dynamic Difficulty Level Adjustment: A Machine Learning Approach

Abstract

Blockchain technology has revolutionized decentralized systems, with applications that span finance, healthcare, and supply chain management. However, scalability challenges, particularly limited transaction throughput and high computational overhead, hinder its broader adoption. This work presents and validates a machine learning-driven consensus mechanism to enhance scalability while preserving security and decentralization. The proposed approach dynamically adjusts the mining difficulty and resource allocation in real time by employing Bayesian-optimized ensemble models (XGBoost and Random Forest) to predict the conditions of the blockchain network. Experimental evaluations show improved throughput, lower latency, and more equitable miner participation compared to traditional Proof of Work (PoW). The findings suggest that data-driven consensus can mitigate long-standing performance bottlenecks, enabling next-generation decentralized systems for industrial-scale deployment.