In today's digital age, social media platforms have become breeding grounds for fake news, false information disguised as legitimate news. This misinformation spreads faster than ever before, influencing public opinion, creating social unrest, and even affecting election outcomes. While fact-checkers work to verify news manually, they can't possibly keep up with the millions of posts shared every minute. The challenge lies in developing automated systems that can quickly and accurately identify fake news at scale. This research aims to develop and evaluate machine learning models that can automatically detect fake news by analyzing linguistic patterns in the text, source reliability indicators, social media engagement metrics, and cross-referencing with verified information sources. The study includes the comparison of different machine learning approaches (including traditional classifiers and deep learning models) to determine which methods work best for different types of misinformation. The findings of this research paper demonstrate that machine learning can effectively identify fake news with high accuracy, especially when combining multiple detection approaches. However, challenges remain in keeping up with constantly evolving misinformation tactics. Future systems will need to incorporate real-time learning capabilities and explainable AI techniques to maintain effectiveness and user trust. These automated detection tools show great promise in helping create a more truthful online information environment.