This paper discusses the growing concern of fake news in the world of digital media, which has transformed public opinion and has developed gradual destruction of trust in reliable sources. We present a solution using machine learning algorithms to find fake news. We used real and fake news articles and then applied different classification algorithms to separate fake news with the real news. We preprocessed the data and split the dataset into training and test sets. Next, we used TF-IDF vectorization. We trained multiple classifiers and evaluated performance. Our results showed that these classifiers are effective in finding fake news articles. In addition, we developed a manual testing mechanism that allows users to input news articles for real-time classification. This work complements ongoing efforts to reduce the growth of fake news and provides the solution for the robust detection mechanisms to ensure information integrity in the digital media.