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: With the exponential growth of digital news sources, accessing relevant and timely information has become a challenge. This project presents the development of a news aggregator system that utilizes web scraping techniques to collect, process, and display news articles from multiple sources in an organized manner. The primary objective is to automate news aggregation, categorize articles based on topics, and present users with accurate, up-to-date information. The system employs web scraping tools such as Beautiful Soup, Scrapy, and Selenium for data extraction, along with backend technologies like Flask/Django and a frontend build with React/HTML. The growing reliance on digital news sources necessitates an efficient method to filter and present information in a consolidated manner. Traditional news aggregation methods rely on manual input or RSS feeds, which limit the diversity and coverage of news content. Web scraping, on the other hand, allows real-time data collection from various sources, ensuring that users have access to the latest updates without any manual intervention. This report provides a comprehensive analysis of the system’s development, covering aspects such as system architecture, methodologies used for data extraction and processing, implementation details, results, challenges faced, and potential future enhancements. The proposed solution integrates multiple functionalities such as keyword-based categorization, sentiment analysis, and user personalization, enabling users to access news based on their interests and preferences. The system is designed to efficiently handle large datasets, maintain data accuracy, and overcome web scraping challenges such as anti-scraping mechanisms and dynamic content loading. Additionally, the project adheres to ethical and legal considerations by ensuring compliance with data usage policies and implementing mechanisms to avoid excessive server requests. Performance analysis and user experience evaluations further validate the effectiveness of the proposed system. The project aims to contribute to the field of automated news aggregation by enhancing accessibility, improving news filtering, and streamlining the presentation of news content.