The development of Online media sites in recent years has led to the spread of content sharing like commercial advertisements, political news, celebrity news, and so on. Various social media applications, such as Facebook, Instagram, and Twitter, have been impacted by fake news. Due to the easier access and rapid expansion of data through online media platforms, distinguishing between fake and real data has become difficult. The massive volume of news transmitted over online media portals makes manual verification impractical, which has prompted the development and deployment of automated methods for detecting fake news. Given the clear dangers of misleading and deception, fake news study has seen an increase in efforts that employ machine learning approaches, and sentiment analysis. In this study, we review the many implementations of sentiment analysis and machine learning methodologies in the fake news detection, as well as the most pressing difficulties and future research prospects.