This chapter investigates the use of stacked ensemble learning to enhance fake news detection by combining the strengths of different machine learning models, such as Support Vector Machines (SVM), Random Forests, and XGBoost. Although each model is commonly applied to classification tasks, their standalone performance may be constrained by specific biases and variance. By integrating these models into a stacked ensemble, the proposed approach aims to build a more reliable and precise system for detecting fake news. We used the ISOT Fake News dataset in this work. The findings reveal that the ensemble model significantly outperforms the single individual models, achieving an impressive accuracy rate of 97.14%, along with notable improvements in recall and F1 score. This research highlights the persuasiveness of ensemble methods in boosting the overall performance of machine learning-based systems for identifying misinformation, offering a valuable contribution to the fight against fake news.