Accurate and accessible weather data visualization platforms are essential for various users, including the general public, agricultural sectors, and emergency services. Traditional methods of accessing and analyzing weather data are often inefficient and complex. This study details the development, testing, and implementation of a comprehensive weather data visualization platform designed to collect, analyze, and present weather data in a user-friendly manner. Utilizing Python and Flask, with MySQL for data storage, the platform integrates data crawling, analysis, and visualization tools. The system incorporates advanced machine learning and deep learning algorithms to enhance predictive accuracy. Rigorous testing phases ensured the robustness of the platform, which features real-time data retrieval, historical data access, and various interactive visualizations. The platform demonstrated high performance, efficiency, and usability, significantly improving the accessibility and understanding of weather data. Future prospects include expanded data sources and advanced analytics capabilities, offering substantial potential for broader applications. This platform represents a significant advancement in leveraging technology for meteorological purposes, enhancing decision-making processes across multiple domains.