The fast proliferation of fake news on social media has sparked worry about its impact on public perception and decision-making. This study uses Social Network Analysis (SNA) to investigate the link between social media use, fake news perception, and diffusion. Using data from 310 active social media users, the study investigates important aspects such as time spent online, trust in social media content, and network centrality. The data show that spending time on social media has little effect on fake news exposure, however increased trust in online platforms considerably promotes misinformation dissemination. Furthermore, fake news propagation is not confined to extremely influential people; it occurs at all levels of network centrality. The study also shows that user attributes help to forecast the spread of fake news, but external impacts like cognitive biases and algorithmic considerations must also be considered. Predictive modeling with logistic regression and decision trees identifies crucial behavioral patterns in misinformation sharing. The decision tree study shows confidence in social media, network centrality, and time spent online as the key causes of fake news spread. The ROC curve study indicates that, while user attributes have some predictive potential, misinformation diffusion is influenced by a variety of external factors. These findings highlight the critical need for media literacy initiatives, stronger fact-checking tools, and better platform policies to combat disinformation. This study adds to the increasing body of knowledge about false news dynamics and offers practical techniques for mitigating its societal impact.