The research presents a study on the formation of intelligent management systems for innovation ecosystems in the context of ensuring Russia’s technological sovereignty under current geopolitical challenges. The research aims to develop theoretical and methodological foundations, along with practical mechanisms, for creating adaptive management systems based on artificial intelligence (AI) and machine learning technologies. The research methodology is grounded in a systems approach and includes a comprehensive set of interrelated indicators for assessing technological sovereignty, developed by the authors. These indicators are temporally differentiated into short-, medium-, and long-term categories, accompanied by algorithms for their calculation and interpretation. The main results are represented by organizational and managerial mechanisms for coordinating participants in the innovation process, a qualified customer model, and the architecture of an intelligent management system that integrates predictive analytics, blockchain technologies, and digital twins. A modular system concept is proposed, comprising components for data collection and verification, analytical processing, decision support, and visualization. The practical significance lies in the potential application of the proposed approaches by federal executive authorities, research organizations, and industrial enterprises to enhance the efficiency of the innovation process management. The novelty of this research is reflected in the development of a methodological framework for assessing technological sovereignty and the creation of a modular architecture for an intelligent management system of innovation ecosystems, ensuring adaptability to changing external conditions and effective coordination of innovation process participants in pursuit of the country’s technological independence.