This project focuses on addressing the issue of frequent hotel booking cancellations, which often lead to operational disruptions and revenue loss in the hospitality industry. It proposes a predictive system that analyzes historical booking data to forecast the likelihood of cancellations using machine learning techniques, specifically Decision Tree and Random Forest algorithms. The system is implemented as a web-based application with separate interfaces for customers and administrators, enabling users to make bookings while allowing hotel managers to monitor cancellation risks and optimize room allocation. Experimental results show that the model effectively identifies cancellation patterns, helping hotels take proactive measures to reduce uncertainty and improve financial performance.