<p>The suppression of wildfires involves making multiple decisions to anticipate fire growth and behavior, which are influenced by various factors in diverse environments. This research aims to identify the conditions that contribute to the ignition and spread of fires, considering different surface fuel characteristics and weather elements. To achieve this, a mixed integer programming model is proposed to calculate the minimum fire arrival time and fire line intensity. Moreover, this model is a systematic framework for analyzing the intricate interplay between environmental factors and wildfire behavior. We investigate a dynamic fire behavior model within a heterogeneous landscape, encompassing diverse elements such as topography, weather conditions, and fuel variables. By accounting for these varying landscape characteristics, our study aims to capture the complex dynamics of fire behavior and provide a comprehensive understanding of how fires propagate within diverse environments. The critical regions are ranked based on the direction fires propagate from the ignition point. Additionally, this study incorporates control locations into the model’s decision-making process to design potential suppression actions based on fire growth and behavior during the preignition stage. The model’s effectiveness is demonstrated through a detailed case study using real data from Sonoma County, California. One notable advantage of the model is its ability to rank critical regions based on their wildfire risks. The analysis reveals that polygons 95– 99, characterized by a high potential risk of active crown fires, are identified as the most effective control locations. This finding aligns with historical wildfire incidents in Sonoma County. The model offers valuable insights for stakeholders in making informed decisions regarding fire prevention and mitigation strategies, aiming to reduce the impact of wildfires in vulnerable regions.</p>