The surge in online hotel bookings has transformed the industry but brought a persistent challenge: cancellations. This study predicts cancellations using the “Hotel Reservation” dataset from Kaggle. Employing data visualization, we unlock insights and address model performance through meticulous pre-processing, including categorical data encoding, handling class imbalance, and removing redundant features. Eight machine learning models are evaluated, with the Random Forest Classifier emerging as the champion, achieving an impressive 93% F1 score and validated by a test score of 93.21%. Our findings provide valuable tools for stakeholders in the tourism and hotel industry, enabling the stakeholders to leverage our findings and devise appropriate measures for mitigating cancellations’ impact, optimizing resource allocation, and bolstering the bottom line.