In this digital age, staying informed has become challenging due to the enormous amount of data availability, which has led to the adoption of easily consumable short form information as an alternative. Hence, the role of summaries i.e., offering concise points that enables readers to get significant relevant information comes in action. News summary not only improves the reader experience and increases engagement, but it also assists traditional news organizations in adapting to the changing digital landscape while maintaining journalistic excellence. Furthermore, headline generation plays an important role for these organizations in terms of capturing and retaining reader interest. News categorization helps these organizations as it systematically categorizes articles, improving information accessibility for users while optimizing news ecosystem. This study attempts to create a complete framework for producing concise summaries and appealing headlines, and classify the provided text into specific categories using Machine Learning and Natural Language Processing models.