Fuzzy Set Theory (FST) is a new theory, introduced as an alternative to classical Set Theory (ST), provides a framework for handling uncertainty and imprecision by allowing membership rather than binary classification. In this study, we propose a novel approach to forest fire risk assessment by utilizing FST to overcome the limitations of classical ST based models. Forest fires cause destruction of thousands of hectares of forestland for Turkey located in the Mediterranean climate zone. As a result of forest fires, entire ecosystem damaged and results show themselves negatively in many dimensions. Therefore, preventing or intervening forest fires is an important situation. Fire risk maps are created in order to prevent forest fires or to be prepared in advance for intervention. Usually, fire risk maps are created with help of equations made by classical ST. However, creating a risk map with classical ST as yes-no may not lead us to exactly the right measures. These created risk maps are not flexible. For this reason, in this study, we aim to determine fire risks from a different perspective with the help of FST and create risk assessment with high efficiency and reality value. Our approach uses FST to assign risk levels based on degrees of membership and aims to allow for more nuanced and realistic risk assessment. By integrating various terrain-related factors into a fuzzy logic framework, it is designed to produce fire risk levels with higher accuracy and better reflect the dynamic nature of fire risk. This method aims to contribute to more effective fire management strategies and minimize potential environmental damage.