The objective of this investigation is to predict hotel booking cancellations by utilizing interpretable machine learning techniques. This will aid hotels in the optimization of resource utilization, the enhancement of revenue management, and the improvement of customer service. In order to identify trends associated with booking cancellations, the research analyzes historical booking data, which includes customer demographics, reservation types, market segments, deposit policies, and previous cancellation behaviors. In order to forecast the likelihood of cancellations, numerous machine learning models are implemented. In order to guarantee that these decisions are understood, explainable AI and feature importance analysis are implemented. The proposed method enables hotel managers to make data-driven decisions by focusing on the primary factors that contribute to guest cancellations and implementing targeted strategies to improve occupancy rates and mitigate revenue loss. Interpretable machine learning is a valuable asset for the hospitality sector, as it enables precise predictions while maintaining transparency and enhancing trust in the process, according to research.