This research paper presents the design and development of an open-source Cyber Threat Intelligence (CTI) platform aimed at improving real-time cybersecurity monitoring and threat analysis. The system addresses the growing challenges of cyberattacks such as ransomware, phishing, data breaches, and zero-day vulnerabilities by automating the collection and analysis of threat data from multiple cybersecurity sources. The proposed platform integrates web scraping techniques using tools such as BeautifulSoup, RSS feeds, and Firecrawl to gather threat-related information. The collected data is preprocessed, structured, and analyzed using keyword-based classification to identify threat categories and severity levels. A MySQL database is used for efficient storage and retrieval, supported by full-text search for quick access to relevant threat information. The system also includes an interactive dashboard that visualizes threats based on severity, categories, and region-specific relevance (with a focus on India-related incidents). Additionally, an AI-powered chatbot assists users in understanding complex threat data by providing simplified explanations and insights. The platform is designed to be cost-effective, user-friendly, and accessible for students, researchers, and small organizations. It reduces manual effort, enhances situational awareness, and supports informed decision-making in cybersecurity environments. Despite challenges such as web scraping limitations and evolving threat patterns, the system provides a scalable foundation for future enhancements, including machine learning-based threat detection and predictive analytics.