Oropharyngeal cancer affects the throat, tonsils, and base of the tongue. It poses a severe challenge to healthcare, as the methods used to treat patients are often proven inadequate. This paper explores the potential of machine learning to improve prediction accuracy for oropharyngeal cancer. Earlier work is reviewed, and limitations are discussed. An ensemble model combining Random Forest, Gradient Boosting, Extra Trees, Support Vector Machine, and Logistic Regression was applied, achieving 98.29% accuracy. This paper explains the processes of preprocessing, feature selection, model creation, hyperparameter tuning, and result evaluation required to achieve this outcome. Other hybrid models were also generated for comparison with the ensemble model. The integration of machine learning in healthcare fields like oropharyngeal cancer will greatly improve patient outcomes and provide more personalized treatment strategies.