This study presents the development and evaluation of an advanced social running platform designed to enhance user engagement through personalized activity recommendations and interactive mapping features. Unlike existing platforms that primarily offer basic tracking functions, our solution integrates external map APIs, sophisticated hybrid recommendation algorithms, and robust social interaction capabilities within a scalable Model-View-Controller (MVC) architecture. Utilizing MongoDB for efficient data management, the platform consolidates key entities such as users, activities, and comments, ensuring data consistency and flexibility. Comprehensive testing was conducted to assess search accuracy, response time, cross-platform compatibility, and security, demonstrating superior performance compared to existing competitors. The recommendation system achieved a precision of 92% and a response time of 0.85 seconds, while the platform efficiently handled over 100 concurrent users and ensured strong data protection measures. These findings underscore the platform’s potential to significantly improve user experience and community engagement in the digital fitness landscape.