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Abstract: Research on Fake news detection considers previous and current methods for fake news detection in textual formats while detailing how and why news fake exists in the first place. This research paper includes various methods and concepts that is to be discussed like on Machine learning algorithms, Network Analysis approaches, and proposes a three-part method using Naïve Bayes Classifier, Support Vector Machines, and Semantic Analysis as an accurate way to identify fake news on social media. There are different social media platforms that are accessible to the users like Facebook , Instagram , Twitter, Whatsapp etc. Any user can make a post that’s misleading or false and spread the false news through these online platforms. These platforms do not verify those users or their posts. In this research paper, we aim to perform binary classification of various news articles available online with the help of concepts related to Artificial Intelligence, Natural Language Processing and Machine Learning. We also aim to provide the user with the ability to classify the news as fake or real and also check the authenticity of the website that is publishing the news. Therefore, this research compares the existing approaches to build the models and with further improvements to be expected by using the combination of different machine learning techniques.