This study proposes a comprehensive methodology for forecasting the dynamics of transformation of scientific human capital (intellectual resources) in the context of the digital economy, with a focus on the problems of migration processes of scientists. Based on machine learning and system dynamics methods, models have been built that allow analyzing scenarios for the development of the situation with the “brain drain” (external migration) and internal migration of scientific personnel in Russia and China. The modeling results, including comparative analysis and identification of key factors of attraction/push, serve as a basis for the formation of priority management decisions aimed at optimizing the innovative transformation of intellectual capital to ensure sustainable development in the digital landscape.