The prevalence of fake news in social media and online platforms has become a growing concern for society. To address this issue, researchers have explored various techniques for fake news detection. The proposed method first preprocesses the news articles using Natural Language Processing (NLP) techniques, including tokenization, stop-word removal, and stemming, to extract relevant features from the text data. These features are then used as input to the decision tree algorithm to classify news articles as real or fake. In this study, we propose a method for fake news detection using decision trees and Ada boost. Decision trees are used to extract relevant features from the text data, and Ada boost is employed to enhance the performance of the decision trees. The proposed method is evaluated on a dataset of news articles, and the results demonstrate its effectiveness in detecting fake news. The proposed method can be used as a tool for identifying fake news and could be beneficial in addressing the issue of misinformation in online platforms. The experimental results show that the proposed method using decision trees achieves high accuracy in fake news detection, which demonstrates the effectiveness of decision trees in feature extraction. Furthermore, Ada boost is shown to improve the performance of the decision tree algorithm by adjusting the weights of misclassified samples, which further improves the overall classification performance. This paper evaluated the proposed method on a dataset of news articles and achieves an accuracy of 99% using decision trees and 82% using Ada boost. This paper demonstrates and analyzes the detection of fake news with popular machine learning algorithms.