This study presents a hybrid risk assessment framework that integrates behavioral finance insights with mathematical modeling to improve investment decision-making. Recognizing the limitations of traditional risk management models, which often ignore psychological factors, this research incorporates behavioral biases such as overconfidence, herding behavior, loss aversion, and anchoring into a quantitative structure using SPSS-based statistical validation and a gray evaluation model. Data were collected from 150 investors and analyzed through multiple regression to assess the predictive impact of behavioral variables on perceived investment risk. The results indicate that behavioral biases significantly influence risk perception, with overconfidence emerging as the strongest predictor. These validated behavioral weights were then embedded into a gray evaluation function, producing a composite risk score that holistically combines both objective financial factors and subjective behavioral dimensions. The inclusion of behavioral risk increased the overall investment risk score, shifting the classification from moderate to medium-high risk. This research contributes a comprehensive, behaviorally informed risk model that enhances the accuracy, realism, and preventive capability of investment risk management strategies in today’s complex and psychologically driven financial markets.