Wildfires pose an increasingly serious threat to human life, property, and the environment. To enhance wildfire monitoring, remote sensing satellite data has become widely used. The static meteorological satellite GK2A has emerged as a research hotspot in this field due to its advantages of all-day coverage, high resolution, and real-time data transmission. In this study, we developed a near-real-time wildfire monitoring model using GK2A remote sensing satellite data and the random forest method. The model algorithm was validated using the Xichang fire event and demonstrated high accuracy and robustness in wildfire monitoring, thereby providing important support for timely warning and response to wildfires. With the advancement of remote sensing technology, the utilization of GK2A satellite data and advanced machine learning algorithms is expected to provide more accurate and efficient methods for early detection and response to wildfires. The overall precision of the experimental results is 0.93, and the F1 score is 0.62. This study significantly contributes to improving the level of wildfire monitoring technology.