International Journal of Scientific Research and Engineering Development

Managing customer flow is a cornerstone of operational efficiency in the service industry, yet traditional "take-a-number" methods often result in human error and increased customer stress due to a lack of real-time status updates. While Digital Queue Management (DQM) systems have automated registration, they remain limited by their inability to accurately predict waiting times. This study addresses these critical inefficiencies by proposing a Smart Queue Management System (SQMS) that integrates an Artificial Intelligence (AI)-based time prediction model with multi-platform accessibility. Developed using an Agile Software Development Life Cycle (SDLC) and a client-server architecture, the system synthesizes an on-site kiosk, a mobile-first web portal, and public digital signage into a unified ecosystem. The analytical core of the system utilizes machine learning algorithms, specifically Random Forest and Gradient Boosting, which are trained on both historical arrival patterns and real-time operational data to generate dynamic Estimated Waiting Times (EWT). Results demonstrate that the SQMS provides real-time synchronization across all platforms, allowing users to transition to virtual monitoring via QR codes, thereby reducing physical congestion and waiting anxiety. Furthermore, the system includes an administrative dashboard that offers data-driven insights into staff efficiency and service bottlenecks. Ultimately, the SQMS transforms traditional waiting environments into streamlined digital ecosystems, enhancing both administrative productivity and the overall quality of the customer experience.

Paul Xander Dela Cruz, Dwayne Lancelot Fuerte, Emerson Daguio Jr, Ms.Vivien Agustin, Dr.Ronald Fernandez,"Smart Queue Management System With AI-Based Time Prediction" International Journal of Scientific Research and Engineering Development, V9(3): Page(1146-1153) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.