Abstract
Large-scale religious places in India experience unpre- dictable visitor surges, especially during festivals and weekends. Traditional queue handling methods are often inadequate for controlling such dynamic crowds, resulting in long waiting hours, discomfort, and operational ineffi- ciencies. This paper presents DarshanEase, an AI-driven smart queue management solution designed specifically for temple environments. The system integrates online booking, walk-in registration, real-time monitoring, dy- namic time-slot allocation, and crowd forecasting using machine-learning models such as XGBoost and LSTM. DarshanEase operates through mobile and web interfaces, enabling devotees to receive updated queue status, time- slot notifications, and wait-time predictions. For temple authorities, the system provides a centralized dashboard for live analytics, decision support, and scheduling adjust- ments. The proposed model significantly reduces conges- tion, improves visitor satisfaction, and supports efficient resource planning.