The article presents the results of a study dedicated to the development of a mobile application for optimizing the training process in cycling. The relevance of the work is driven by the growing demand for digital tools in the sports industry. It has been established that existing solutions are characterized by limited functionality, dependence on stable internet connectivity, and insufficient detail in the collected data. Systemic shortcomings of modern platforms have been identified, including architectural limitations and ergonomic miscalculations in user interfaces. The research methodology was based on a comparative analysis of the sports application market. Ranking of functional requirements was performed using Saaty’s Analytic Hierarchy Process (AHP). A client-server application architecture with offline mode and Bluetooth device integration has been developed. Local storage of training metrics (heart rate, speed, altitude) has been implemented using SQLite DBMS. The ability to export routes in GPX format has been ensured. Testing of the prototype demonstrated stable operation of all application modules. The quality of the solution has been confirmed by positive user feedback, noting the interface accessibility, depth of analytics, and reliability in offline mode. The developed product eliminates key limitations of analogues through independence from internet connection and enhanced data visualization. The research results confirm the effectiveness of the AHP method for designing sports digital solutions. The proposed approach lays the foundation for implementing machine learning algorithms in training load forecasting. The work has interdisciplinary significance, combining achievements in information technology and sports science. The obtained conclusions contribute to the development of intelligent ecosystems in the field of physical culture and sports telemetry.