This research presents a comprehensive fuzzy logic model for economic risk assessment and investment decision-making, addressing the inherent uncertainty and imprecision in financial markets. Traditional economic models often struggle with vague linguistic variables and subjective expert knowledge, making fuzzy logic an ideal framework for capturing the complexity of economic decision-making processes. The proposed model incorporates four critical input variables: market volatility (0–100%), economic indicators score (0–10), financial liquidity ratio (0–5), and debt-to-equity ratio (0–3). These inputs are processed through triangular membership functions with linguistically interpretable labels ranging from “Low” to “VeryHigh.” The system generates two output variables: investment risk level (0–100) and investment decision score (0–10), providing both quantitative risk assessment and qualitative investment recommendations. The fuzzy inference system employs 22 carefully crafted rules based on expert knowledge and economic principles, implemented using MATLAB’s Fuzzy Logic Toolbox. The Mamdani inference method ensures interpretable results while maintaining computational efficiency. Comprehensive testing demonstrates the model’s effectiveness across various economic scenarios, from conservative low-risk investments to high-volatility market conditions. Results indicate that the fuzzy logic approach successfully captures the nuanced relationships between economic variables, providing more robust decision support compared to traditional binary classification methods. The model's ability to handle uncertainty and provide graduated responses makes it particularly valuable for financial institutions, investment advisors, and economic analysts seeking sophisticated risk assessment tools in volatile market environments.