###### Abstract  
Earthquakes, as one of the most destructive natural disasters, often cause significant casualties and severe economic losses. Accurate prediction of earthquake fatalities is of great importance for pre-disaster prevention and mitigation planning, as well as post-disaster emergency response deployment. To address the challenges of small sample sizes, high dimensionality, and strong nonlinearity in earthquake fatality prediction, this paper proposes an integrated modeling approach (PCA-IWOA-XGBoost) combining Principal Component Analysis (PCA), the Improved Whale Optimization Algorithm (IWOA), and Extreme Gradient Boosting (XGBoost). The method first employs PCA to reduce the dimensionality of the influencing factor data, eliminating redundant information and improving modeling efficiency. Subsequently, the IWOA is used to intelligently optimize key hyperparameters of the XGBoost model, enhancing the prediction accuracy and stability. Using 42 major earthquake events in China from 1970 to 2025 as a case study, covering regions including the west (e.g., Tonghai in Yunnan, Wenchuan, Jiuzhaigou), central (e.g., Lushan in Sichuan, Ya'an), east (e.g., Tangshan, Yingkou), north (e.g., Bautou in Inner Mongolia, Helinger), northwest (e.g., Jiashi in Xinjiang, Wushi, Yongdeng in Gansu), and southwest (e.g., Lancang in Yunnan, Lijiang, Ludian), the empirical results showed that the PCA-IWOA-XGBoost model achieved an average test set accuracy of 97.0%, a coefficient of determination (\(R^{2}\)) of 0.996, a root mean square error (_RMSE_) and mean absolute error (_MAE_) reduced to 4.410 and 3.430, respectively, and a residual prediction deviation (_RPD_) of 21.090. These results significantly outperformed the baseline XGBoost, PCA-XGBoost, and IWOA-XGBoost models, providing improved technical support for earthquake disaster risk assessment and emergency response.  
202515866020  
Pre-earthquake disaster prevention and preparedness, as well as rapid and accurate post-earthquake disaster assessment to scientifically guide emergency rescue operations, are of paramount importance in China's current earthquake prevention and mitigation efforts. Earthquake casualty assessment, as a critical component of disaster prevention and mitigation, can evaluate the risk of human casualties in cities or regions before an earthquake, providing references for seismic design and the emergency preparedness of engineering structures. Additionally, it can rapidly assess the number and distribution of earthquake-related deaths after an event, offering technical support for emergency rescue operations [6; 7; 8; 9; 10].  
Given the significance of earthquake casualty assessment, scholars from various fields have conducted extensive research on earthquake casualty assessment models from different perspectives. Traditional methods for assessing earthquake casualties can be divided into casualty models based on seismic parameters and those based on building vulnerability, depending on whether the impact of building damage on casualties is considered. With the continuous advancement of artificial intelligence technology, machine learning methods have been widely applied in the field of earthquake research for assessing death tolls, enhancing the accuracy and speed of such evaluations. This has also proven effective in pre-earthquake prevention and emergency rescue operations for disaster reduction and mitigation.