The growing demand for energy and the need to reduce emissions have made hybrid renewable energy systems (HRES) a strategic priority. Among these systems, the integration of solar, wind, and fuel cell resources in a complementary structure offers significant potential for reliable and low-emission energy supply. However, optimal site selection for deploying such systems – particularly in countries with diverse climatic and contextual conditions like Iran – presents a multi-criteria challenge accompanied by inherent uncertainty. This study proposes an integrated decision-making framework for prioritizing solar–wind–fuel cell hybrid systems across Iran’s 31 provincial capitals. The technical, economic, and environmental performance of the hybrid system was simulated using HOMER, generating indicators such as Cost of Energy (COE), Net Present Cost (NPC), CO2 emissions, renewable energy share, and capacity factors. These indicators, along with contextual criteria like population, natural disaster risk, and land price, were integrated into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank the provinces. To assess robustness, an uncertainty analysis based on Monte Carlo simulation (10,000 iterations) was conducted. Results indicate that Tehran Province exhibits the highest closeness coefficient (~0.69) with a greater than 90% probability of being ranked among the top five. Conversely, regions such as Kerman and Sistan and Baluchistan consistently obtained lower ranks across most scenarios. This study demonstrates that integrating TOPSIS with Monte Carlo simulation provides not only quantitative rankings but also a risk-informed decision-making tool, effectively supporting policymakers in the siting of pilot renewable energy projects at the national scale.