Fake News Detection is a text classification subtask that entails determining if news is authentic or fake. “Fake news” is the name given to information that is presented as news but is actually incorrect or misleading. It seeks to trick or trick people. We wish to aid society by analyzing and concluding the news as true or fake with our work because there has been a sharp surge in fraud news across the globe. The research proposal makes use of NLP techniques to spot “fraud news,” or inappropriate news items that arrive from various resources. By building a model with ML techniques like Logistic Regression, Naive Bayes, and Decision Tree, fake news can be detected. The research study shows various properties which can be used to differentiate fraud contents from real. By using textual properties, the combination of various ML algorithms utilizing different methods of ensemble and evaluating their performance on real word datasets.