This study explores the development and application of specialized investment risk indicators and corresponding calculation models tailored for innovation project financing. Given the high degree of uncertainty, technological volatility, and multidimensional risks associated with innovation projects, traditional financial evaluation methods are often insufficient. Drawing on international best practices and leveraging tools such as Deep Learning, real-time data analytics, and simulation-based modeling, the study proposes a structured framework for identifying and quantifying financial, technical, market, and regulatory risks. The comparative analysis of traditional versus AI-based risk evaluation models highlights the superior adaptability, predictive power, and decision-making support provided by dynamic risk indicators. The findings offer both theoretical and practical contributions for institutions aiming to modernize their investment governance systems, particularly in innovation-intensive sectors.