Due to the COVID-19 pandemic, several health and economic challenges come to play awkwardly. This has introduced misinformation and confusion around the globe. The issues of fake news have attained an increasing eminence in the diffusion of shaping news stories. Many of them stop to depend on the newspapers, magazines, etc and started to rely on social media completely. Social media became the main news source for millions of people due to their easy access, cheap, more attractive and rapid dissemination. The fake content started to spread at a large pace to gain popularity over social media to distract people from the current critical issues, in some occasions spreading more and faster than the true information. People spread fake news on social media for financial and political gain. Fake data in all forms need to be detected as soon as possible to avoid a negative impact on society. This project makes an analysis of the research related to fake news detection; we trained and tested different machine learning algorithms separately to demonstrate the efficiency of the classification on the dataset. This project was implemented in the Jupiter notebook platform and performance was evaluated.