This study analyzes the value and risk of Iranian startups in the face of financial and operational uncertainties. The aim of the study is to provide a risk-based and realistic framework for valuing startups and supporting decision-making for investors and founders. In this study, quantitative data was collected from 10 to 20 selected startups in the fields of information technology, digital services, e-commerce, fintech, and digital health. Key variables including revenue, growth rate, burn rate, CAC, churn rate, lifetime value (LTV), and EBITDA margin were modeled. To analyze uncertainties, a Monte Carlo simulation was run with 1000 iterations and the probability distributions of cash flow and net present value (NPV) of the startups were extracted. Sensitivity analysis and Tornado chart showed that revenue growth rate, CAC, and burn rate have the greatest impact on the value and risk of startups. Also, pessimistic, baseline, and optimistic scenarios shed light on the range of value probabilities and the probability of liquidity shortage. The results show that the use of probabilistic analysis and Real Options models allows for phased planning and flexible management, reduces investment risk, and makes investors and founders’ decisions more informed. The findings emphasize that advanced valuation models, scenario analysis, and risk tools are effective tools for managing uncertainty in the startup ecosystem and can provide a foundation for future research utilizing artificial intelligence and real-time data.