Our study is guided by two main objectives: 1. `To identify common linguistic, stylistic or structural patterns in differentiating real and fake news articles.` We analyze word usage, bigram patterns, and readability scores to uncover how fake news differs from real news in language style, structure, and complexity. For instance, fake news may use more media-related terms or web-like formatting, while real news emphasizes verified sources and geographic references. 2. `To classify whether a news article is fake or real based on its content and title.` Using machine learning models (GLMNet and XGBoost), we train classifiers on textual features from the content and titles. The goal is to develop a predictive model capable of accurately labeling unseen articles as real or fake, based on learned patterns in the dataset.