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Abstract: The rampant spread of misinformation on platform like Facebook poses a critical challenge to public discourse. This proposed system focuses on detecting fake newsand fake images using machine learning algorithms. To develop a robust machine learning-based system for detecting fake news specifically on Facebook. By leveraging techniques like Logistic Regression and image analysis models, the project aims to identify patterns that distinguish between real and deceptive content. The system processes textual and visual data to flag misinformation, enhancing the reliability of online information. The system utilizes a combination of text-based and image-based machine learning techniques to achieve this goal. For fake news detection, a Logistic Regression algorithm is employedto classify news articles as either real or fake. The model is trained on a label dataset containing various features extracted from the news articles, such as word frequency, sentiment analysis, and metadata. By learning from these features, the model can effectively identify patterns that are common in deceptive or misleading news content. On the image analysis front, the proposed system incorporates advanced image recognition andmanipulation detection algorithms. Techniques like Convolutional Neural Networks (CNNs) are used to detect anomalies in images that may indicate forgery or tampering.