###### Abstract
The problem is that accurately identifying the number of fires in the United States is difficult due to the frequent occurrence of disasters, large fires, and terrorism. To be specific, urban fires have been identified as a serious problem, as can be noted by the death of 492 people in a fire at the Cocoanut Grove nightclub in Boston in the 1940s and 61 people in a fire at the LaSalle hotel in Chicago. Frequent fire incidents in urban factories in South Korea have caused severe human casualties and property damage. According to a report released by the National Fire Agency (NFA) of Korea, 12,645 factory fires occurred over five years from 2016 to 2020, resulting in 900 casualties (70 deaths and 830 injuries). In the fire investigation and reporting regulations of South Korea, large fires are defined as the fires that cause more than five deaths or ten casualties or those that cause property damage of more than five billion KRW. For example, two fire incidents that broke out in factories have been classified as large fires, such as the fire at an electronics factory on August 21, 2018 that caused nine deaths and six injuries, and another at a logistics center in 2020 that resulted in 38 deaths and 12 injuries. As reported in the media, fires have frequently occurred in domestic industrial complexes because of the spread of fire caused by their dense structure and several explosions caused by chemicals. Furthermore, these have been reported to cause secondary damage to surrounding areas. Regarding factory fires that cause numerous human casualties and property damage, damage can be minimized through various means, such as early fire suppression, occupant evacuation plans, and securing the stability of structures. Above all things, fire safety can be significantly improved by preparing more economical and efficient management strategies for fire prevention. Specifically, predicting property damage caused by fire is fairly difficult. For example, considerable time and manpower are required to secure data [5]. The research question of this study is as follows: can the degree of property damage in the event of a factory fire be predicted through a machine learning model learned using simple data such as building register information and fire scenarios? The size of the fire, the size of combustibles in the building, quick detection, and first responders have a significant impact on the size of the fire. However, this information is impossible to obtain before a fire occurs. However, this study begins with the hypothesis that when building information and fire scenarios for numerous fire events are learned, excluding information that cannot be secured, trends in fire size will be evident. Thus, this paper's primary objective is to propose and verify a framework for predicting the property damage scale caused by fire using machine learning (ML) with simple data. Korean public datasets are collected and used as training data for ML algorithms, and the accuracy of the proposed method is verified. The remainder of this paper is organized as follows. Section 2 presents the trend of previous ML research related to fire to set the direction of ML development. Section 3 presents the material and methods (preprocessing of learning data and developing ML models). Section 4 presents results and conclusions derived from the development of a fire data-based property damage rating classification model. Finally, Section 5 presents the conclusions.