A Machine Learning Augmented Cooperative-Game Framework for Blockchain and Non-Fungible Token-Based Artwork Trading with Zero-Knowledge Proofs

In the context of smart cities, Non-Fungible Tokens (NFTs) are transforming digital art markets by enabling secure, decentralized transactions. As NFT trading grows, incorporating intelligence and adaptability becomes crucial—making Machine Learning (ML) integration essential. However, existing models, particularly Cooperative Game Theoretic Trading (CoGTT) frameworks, underutilize ML across all trading phases. Key gaps include limited real-time adaptability, suboptimal negotiation strategies, and inadequate buyer–seller matchmaking. This research addresses these gaps by integrating ML into a three-phase CoGTT framework—ML-augmented Naive Trading, Min–Max Price Negotiation, and Equilibrium-Based Trading—to enhance decision-making and pricing. The methodology applies ML algorithms such as decision trees, clustering, and reinforcement learning (Q-learning) within a public blockchain–based simulation environment using smart contracts. The simulation uses a customized dataset reflecting both market dynamics and artist credibility. The dataset is synthetically generated to emulate an NFT marketplace while maintaining controlled experimental conditions, which may limit direct applicability to volatile real-world markets. Zero-knowledge proofs (ZKPs) are employed to preserve privacy. ZKPs are employed to preserve privacy. A comparative analysis of ML models for NFT price estimation and strategic bidding demonstrates the effectiveness of combining predictive algorithms with reinforcement learning. Linear Regression and Random Forest models both accurately estimate NFT prices, with Random Forest achieving higher real-time prediction accuracy (R2 = 0.9920). K-Means clustering effectively segments market participants to support targeted negotiation, achieving a silhouette score of 0.8178. Integrating Q-learning with Random Forest enables dynamic bidding strategies that minimize the gap between recommended and actual prices. The discrete action set (decrease, stay, increase) supports interpretable, real-time bid adjustments. These findings highlight the potential for ML-driven NFT trading systems to support scalable, privacy-compliant digital marketplaces in smart cities, aligning trading behavior with market demands through automated, data-driven processes.