In an era where fake news spreads rapidly, understanding its dynamics has become a critical challenge. This paper explores the modeling of false information diffusion using the SIR, SEIR, and SEIZ models, applying them to three real-world fake news cases: the New Year’s Day earthquake rumor in Japan, a false claim about former President Obama’s injury, and a doomsday prediction. MATLAB software is used to study and validate the models, offering valuable insights into the factors that influence public responses to untrue information across various social settings. Out of the models, SEIZ fitted the best to the available data in comparison with to the SEIR and SIR models, due to its greater complexity, which stems from the addition of a new population and parameters. The results reveal how these variables affect the spread of false information, underscoring the effect of emotional and cultural factors. The study also points out some limitations, such as the oversimplification of real-world behavior and the limited number of fake news cases analyzed. Understanding the dynamics of false information is paramount and further research studies should be undertaken in order to target wider case studies in terms of themes and geographical context.