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Abstract: Fake news is pervasive on social media and in other forms of media, and it is a serious worry since it has the potential to devastate society and the country. On finding it, a lot of research has already been done. This analyses the research on fake news detection and investigates the best traditional machine learning models in order to build a model of a product with supervised machine learning algorithm that can categories fake news as genuine or false. Features will be extracted as a result of this approach. As this library includes practical methods like Count Vectorizer and Tiff Vectorizer, we suggest utilizing Python to carry out tokenization and feature extraction of text data.