This paper presents an automated system for summarizing and filtering news articles based on sentiment analysis. The system leverages Python-based tools to fetch news headlines using the Google News API, scrape article content using Beautiful Soup and Newspaper3k, and select optimal content through similarity scoring with the sentence-transformers/all-MiniLM-L6-v2 model. Summarization is performed using the Llama 3.2 3B model, while sentiment classification is achieved using the cardiffnlp/twitter-roberta-base-sentiment-latest model. The processed data is stored in Firebase and accessed via an Android app, enabling users to filter negative news and select preferred categories. The system processes 35–40 news articles in 10–11 minutes, significantly outperforming manual efforts. This approach enhances efficiency in news consumption while ensuring scalability across six categories: world, nation, business, technology, entertainment, and sports.