Iranian startups operate in an environment characterized by high uncertainty, economic volatility, resource constraints, and rapid technological changes. In such conditions, the most critical managerial challenge is to design decision-making mechanisms capable of simultaneously managing two contradictory logics: risk control and innovation development. Existing approaches have largely treated these two dimensions separately, which has led in practice to reduced organizational agility or increased vulnerability.The aim of this study is to design an intelligent decision-making model for the simultaneous management of risk and innovation in Iranian startups using the grounded theory approach, in order to provide a localized and practical framework for improving decision quality under uncertainty.The research method is qualitative and based on grounded theory. Data were collected through semi-structured interviews with experts in the startup and technology ecosystem and continued until theoretical saturation was reached. Data analysis was conducted in three stages of open, axial, and selective coding, ultimately resulting in a paradigmatic model of relationships among concepts.The findings indicate that intelligent decision-making in startups emerges from the simultaneous interaction of individual, organizational, and environmental factors, and is effective only when data-driven decision-making, cognitive capabilities, flexible organizational structure, and information governance are integrated. The results also show that a dynamic balance between risk and innovation is a key condition for startup survival and growth.The main contribution of this study lies in presenting a localized, grounded, and actionable model of intelligent decision-making that can serve as a practical framework for managers and policymakers in the startup ecosystem.