Optimizing Cost-Efficient Payment Transactions: AI-Driven Routing Strategies for Reducing Payment Costs

The growing demand for cost-efficient digital transactions has driven the need for scalable and low-cost payment solutions. Traditional blockchain-based transactions suffer from high fees and slow processing times, making decentralized off-chain payment networks a promising alternative. In this paper, we propose SpeedyMurmurs, an AI-enhanced decentralized routing algorithm that significantly reduces payment processing costs and transaction delays. Our approach optimizes payment routing efficiency through embedding-based path discovery, reducing routing overhead by up to two orders of magnitude and cutting transaction processing times by over 50 percent compared to existing blockchain networks. By leveraging machine learning-driven transaction optimization, our system dynamically selects the most cost-effective paths for digital payments while maintaining user privacy and security. Experimental results demonstrate that SpeedyMurmurs reduces transaction fees and computational costs, making decentralized payment systems more financially viable. This research highlights the role of AI-powered routing strategies in minimizing costs and improving efficiency in modern payment networks.