Wildfire risk assessment is essential for managing and mitigating the effects of wildfires, especially in regions frequently affected by severe fires This study focuses on optimizing the National Fire Danger Rating System (NFDRS) by creating 11 custom fuel models tailored to the climatic and vegetation conditions of Golestan province, NE Iran. These models were developed by sampling 125 homogeneous zones. The number of custom fuel models was determined by K-means analysis with relative squared Euclidean distances and the silhouette method using data collected from the homogeneous zones. The NFDRS outputs include the Spread Component (SC), Energy Release Component (ERC), and Burning Index (BI), with fire danger classes identified using the Static Fire Danger Index (SFDI). The models were evaluated using Overall Accuracy, Kappa, Sorensen, True Positive Rate (TPR), False Positive Rate (FPR), and Area under the Curve (AUC). The results revealed significant variability in fuel properties between vegetation types, affecting SC and ERC values. The custom fuel models outperformed the standard NFDRS models in accuracy, with Overall Accuracy (0.85), Kappa (0.78), and AUC (0.92) compared to the NFDRS's 0.70, 0.65, and 0.80, respectively. This study provides a clear illustration of the improved predictive performance of the custom models in the study area, which can help fire managers make more informed decisions, leading to better preparation and more efficient allocation of resources. these models can help mitigate wildfires' economic and environmental impacts. In addition, this research highlights the need for region-specific, customized fuel models to improve the accuracy of fire risk