This document proposes a machine learning model to detect fake news using a passive aggressive algorithm, term frequency–inverse document frequency (TF-IDF), and a convolutional neural network (CNN). The model is trained on a fake news dataset containing both real and fake news. TF-IDF is used to remove stop words before training and testing the model using a passive aggressive classifier, which reacts aggressively to misclassifications. The model can accept text or image inputs, processing images with CNN. The proposed approach aims to accurately categorize news as real or fake.