Exploring the Synergy Between Ethereum Layer 2 Solutions and Machine Learning to Improve Blockchain Scalability

Blockchain technologies, despite their profound transformative potential across multiple industries, continue to face significant scalability challenges. These limitations are primarily observed in restricted transaction throughput and elevated latency, which hinder the ability of blockchain networks to support widespread adoption and high-volume applications. To address these issues, research has predominantly focused on Layer 1 solutions that seek to improve blockchain performance through fundamental modifications to the core protocol and architectural design. Alternatively, Layer 2 solutions enable off-chain transaction processing, increasing throughput and reducing costs while maintaining the security of the base layer. Despite their advantages, Layer 2 approaches are less explored in the literature. To address this gap, this review conducts an in-depth analysis on Ethereum Layer 2 frameworks, emphasizing their integration with machine-learning techniques, with the goal of promoting the prevailing best practices and emerging applications; this review also identifies key technical and operational challenges hindering widespread adoption.