Our project addresses the challenge of distinguishing authentic news from misleading content in today's rapidly evolving digital landscape. Utilizing advanced machine learning models—SVM, Logistic Regression, Naive Bayes, and XGBoost—we compared their efficacy in accurately classifying news articles into four categories: Human Real, Human Fake, Machine Real, and Machine Fake. Our results highlight SVM's accuracy and stability, making it the most effective model for this task. By enhancing the detection and prevention of misinformation, our project significantly contributes to public access to credible news, ensuring that individuals can make informed decisions in a world increasingly influenced by AI-generated content. This effort not only enhances public information integrity, but also addresses the essential need for instruments that can prevent the sophisticated spread of false news.