News articles serve as a window into various subjects’ updates and details. From politics to sports, world affairs to business insights, entertainment to education, news articles cover them all. People read them daily to learn about situations or to get updates on existing matters. Nowadays, automated news classification techniques are applied widely for fast and efficient news categorizations. Models created using ML, like Bayesian models, RF, Logistic Regression, and SVM make it easier to classify a list of articles into different text categories with high accuracy. In the “News-Scope” project, we step into building efficient and reliable ML models to segregate news articles to their respective categories (World, Sports, Business, and Science/Technology News) by understanding their content. The models built shall facilitate news publication media, providing them with valuable insights like useful news recommendations to their users and tools to optimize their content dissemination strategies in the digital age. It also shall be of great help to the online news readers to search and find articles on particular categories promptly.