Fake news, defined as intentionally false information circulated through digital platforms, poses a significant threat to
information integrity, particularly on social media. Given its persuasive nature, it is critical to develop efficient methods for
detecting fake news and promoting more responsible consumption of online content. This paper proposes a model that
leverages supervised machine learning algorithms to detect fake news in online sources. The dataset used in this study,
"fake_or_real_news," was preprocessed through feature extraction, narrowing down the data to two columns: text and labels.
The label column was further processed to mark true news as "REAL." The study applied three different supervised machine
learning algorithms for model training: Logistic Regression, Support Vector Classifier (SVC), and Multinomial Naive Bayes
(MultinomialNB). The models were evaluated based on accuracy, with Logistic Regression achieving the highest accuracy at
91.9%, followed by MultinomialNB at 88.5%, and SVC at 86.8%. Based on the results, the study recommends using Logistic
Regression as the most effective algorithm for detecting fake news in online environments. This approach offers valuable insight
into how machine learning can be employed to combat misinformation