In the contemporary digital landscape, the proliferation of fake news presents a formidable challenge, propagating misinformation at an alarming pace across online platforms. In response, this paper presents an innovative strategy for the classification of fake news, harnessing cutting-edge machine learning methodologies. Our approach integrates BERT, a state-of-the-art transformer-based model, alongside an ensemble comprising Naive Bayes, Passive Aggressive, and Logistic Regression classifiers. By employing this sophisticated ensemble model on the extensive WELFake dataset, comprising over 70,000 news articles, we achieve a remarkable accuracy rate of 95%. Moreover, we complement our classification framework with advanced natural language processing (NLP) techniques, including text summarization. Utilizing a transformer model from Hugging Face, we further categorize news articles into distinct classifications such as acceptable, inappropriate, offensive, and violent. The efficacy of our approach is evident in its ability to discern fake news effectively, providing a robust mechanism for identifying and combating misinformation online. Furthermore, our methodology offers valuable insights into potential avenues for enhancing the detection of fake news in the future. By integrating state-of-the-art machine learning techniques with NLP methodologies, our research contributes to the ongoing efforts to mitigate the adverse impacts of fake news on society and underscores the importance of technological innovation in safeguarding the integrity of information dissemination channels.