In today's fast-paced digital world, fake news spreads like wildfire, making it more critical than ever to identify misinformation quickly. Traditional methods, trained on static datasets, struggle to keep up with the ever-evolving tactics of fake news. This research explores whether Large Language Models (LLMs), with their ability to access and process real-time information, can offer a solution. Researchers put traditional offline models and cutting-edge online LLMs like ChatGPT, Claude, Llama, and Gemini to the test, using a dynamic dataset of real-time news from platforms like Twitter and PolitiFact. The results? Traditional models, while effective on older data, faltered when confronted with emerging narratives. In contrast, the online LLMs showed a remarkable ability to adapt and identify fake news as it surfaces, leveraging their vast knowledge and real-time access to information. Llama and ChatGPT led the pack in accuracy, demonstrating the potential of LLMs to become powerful tools in combating misinformation. While some LLMs struggled with specific topics like politics, the overall findings highlight a shift from static to dynamic AI in the ongoing battle against fake news. This research suggests a future where AI can provide not just quick identification of fake news, but also nuanced explanations of *why* news is considered false, promoting transparency and trust in the digital information ecosystem. However, it also underscores the ongoing need for research, development, and refinement to ensure these tools remain effective and relevant in the ever-changing landscape of misinformation.