The rapid proliferation of fake news has created an urgent need for effective real-time detection systems that can safeguard the integrity of information. This paper presents a hybrid detection framework combining style-based and knowledge-based methods within a user-friendly Chrome extension, called Fake News Busters. Our system utilizes a fine-tuned Llama 3 model to analyze linguistic patterns and integrates external verification through web-scraping and APIs such as Google Fact Check, Google Scholar, and LinkedIn to assess factual claims and source credibility. The backend leverages Flask and PyTorch for scalable processing, while the front-end enables real-time user feedback. Evaluation in benchmark data sets and through a usability study demonstrates that combining linguistic features with external knowledge improves detection accuracy and user trust. This work highlights the importance of hybrid approaches in combating sophisticated misinformation and sets the foundation for user-centered scalable fake news interventions.