The surge of fake news on digital platforms presents
a pressing societal concern, undermining trust and decisionmaking
processes. The reliability of information, crucial for
individuals and societies, faces unprecedented challenges. The
rapid evolution of fake news tactics exacerbates this problem,
demanding constant adaptation of countermeasures. In
response, this study proposes an innovative solution: a userfriendly
browser plugin employing machine learning for realtime
fake news detection. We conduct a thorough examination
of existing techniques, evaluating various algorithms to enhance
accuracy. Through rigorous data preparation and algorithm
refinement, we achieve significant improvements, emphasizing
the importance of textual features and class balancing. The
research extends beyond theory with the development and deployment
of a practical browser plugin, enabling users to actively
combat misinformation. Ethical, legal, and social considerations
are integral, ensuring responsible deployment, bias mitigation,
and adherence to copyright. The study advocates for ongoing
refinement, highlighting the persistent relevance of fake news
detection in an information-driven society.