Traditional trade war analysis often relies on deterministic models that fail to account for the complex, uncertain interactions between Tariffs, GDP, total trade between countries, and their trade balance. Inspired by ecosystem dynamics, this study extends the fuzzy Lotka-Volterra model, originally designed for predator-prey relationships, to geopolitical risk assessment in the era of intensifying trade wars and conflicts affecting global business operations. By representing risk factors such as tariffs, trade sanctions, trade deficits, and conflict escalation as fuzzy variables, the model can capture the uncertainty inherent in international relations. Using trapezoidal fuzzy numbers and alpha-cuts, the framework provides a range of possible tariff scenarios rather than single-point estimates. This approach enables decision-makers to assess the upper and lower bounds of trade sanction threats, offering robust insights for policy planning, investment strategies, and risk mitigation. The model’s applicability is demonstrated through a numerical case example on trade war tensions, highlighting its ability to simulate risk interactions dynamically. By adapting an ecological model to the realm of international affairs, this study tackles a gap in risk management, providing a novel tool for forecasting and response planning in uncertain geoeconomic and geopolitical environments.