International Journal of Scientific Research and Engineering Development

In modern service environments such as banks, hospitals, and customer service centers, queue management has become a major challenge due to increasing customer demand, long waiting times, and inefficient resource utilization. Traditional queue systems are static and lack the ability to predict waiting times or adapt to changing conditions, leading to poor customer experience and operational inefficiencies. This paper presents QueueMindAI, a smart solution using a Multi-Agent Artificial Intelligence System to improve queue management. The system consists of multiple intelligent agents that work together to monitor queue conditions, predict waiting time, detect congestion levels, forecast future trends, and provide optimized decisions such as staff allocation and best branch selection, coordinated by a central supervisor agent. The system is developed using a React-based frontend and a FastAPI backend, and includes a digital twin simulation module and a voice-enabled AI assistant. Experimental evaluation shows the system achieves a classification accuracy of approximately 92–94% in predicting queue congestion and waiting-time categories, confirming its reliability for real-time deployment.

Aishwarya A, Anusri S, Harini K, Harini R, Kanigavarshini R S, "QueueMindAI: A Multi-Agent Artificial Intelligence System for Intelligent Queue Management" International Journal of Scientific Research and Engineering Development, V9(4): Page(830-833) July-August 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.