 This paper proposes a comprehensive evaluation framework to assess wildfire susceptibility and its effects on the ecological environment and urban development. It uses multiple indicators such as temperature, soil type, land use, distance to roads, and slope to evaluate the susceptibility of different areas to wildfires. Additionally, it uses the remote sensing ecological index, RCI, and the nighttime index, and TLI, to measure the vulnerabilities of the ecology and urban development, respectively. The results of the evaluation framework are then integrated to assess the overall risk of wildfire disasters in Gillan. The XGBoost model was found to have the highest predictive performance, with an AUC value of 0.927, accuracy value of 0.863, and RMSI value of 0.327. The results indicate that the XGBoost model has the best predictive performance and can be used to identify and assess the risk of wildfire disasters in Gillan. This article was authored by WhitingU, Chowrin, Yuezhi Liang, and others. We are article.tv, links in the description below.