An increase in the utilization of the Internet has made people rely on social networks to acquire information from various sources. As information comes from different sources, it is always di-cult to verify the credibility of the news. Fake news is defined as the intentional or unintentional spread of news that negatively impacts society. Fake news is always associated with cognitive and psychological issues, and spreaders always target the psychological aspects to make users believe and propagate it further. Qualitative or Quantitative approaches are used for modeling fake news, and this paper uses the quantitative. approach for defining fake news. Propagation dynamics are captured with the help of diffusion models, and this paper focuses on constructing modified epidemic models to model and predict fake news. The proposed models aid in examining the spread of fake news within online social networks. Individuals in the network may potentially fall into one of six states: Susceptible, Exposed, Infected, Recovered, Deceased, or Skeptical. Differential equations are solved, and graphs are modeled for initial conditions and values. Further, the Basic Reproduction Number is calculated to estimate the spread of fake news into the network. Numerical simulation is also shown for various epidemic models.