Museums function as essential institutions that safeguard cultural heritage, but their existing ticketing systems force visitors to wait in long lines while entering their premises. This paper presents an AI-enabled multilingual chatbot-based museum ticket booking system integrated with analytics and machine learning for crowd prediction. The system uses Natu- ral Language Processing (NLP) technology to provide users with three functions which include ticket booking and information access and secure payment process- ing. The system uses a client-server architecture that separates its components into four parts which include chatbot processing and booking management and pay- ment integration and an administrative dashboard. The dashboard system delivers real time information about visitor movement patterns and booking activities. A Random Forest Regression based model uses historical data to forecast upcoming visitor numbers at different time slots, which allows for better crowd management and planning operations. The proposed system reduces manual effort, improves operational efficiency, and en- hances the overall visitor experience.

This paper presents an AI-powered, multilingual chat- bot system designed to simplify museum ticket booking while providing strong visitor analytics. The system in- tegrates an automated platform which delivers smooth operations through its combination of chatbot interaction secure payments and real-time insights while it replaces the slow and confusing conventional methods. The Rasa- powered chatbot uses natural language processing to en- able users with basic dialogue skills to book tickets and get information and make payment transactions. The system offers multilingual support to help different types of vis- itors access its features. The admin portal enables staff members to handle bookings and payments and visitor information throughout the system. The system includes a crowd prediction model based on Random Forest Regres- sion which uses historical data to predict future visitor patterns and enhance operational planning. The analytics module provides detailed information about how users interact with the system and how the system performs which helps organizations make decisions more quickly and accurately. The system uses an intelligent system to create a scalable solution which decreases manual work and boosts operational efficiency while providing better visitor experiences.

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