This study investigates the risk and prediction of job burnout among nurses responding to public health emergencies. A cross-sectional survey was conducted in Xiangyang City, China, involving 1584 working nurses. The Impact of Events Scale-Revised (IES-R) and the Chinese version of the Maslach Burnout Inventory-General Survey (MBI-GS) were used to assess PTSD and burnout levels. Logistic regression analysis identified risk factors, and a nomogram was developed to predict burnout risk. The nomogram demonstrated good calibration and discrimination, with an area under the receiver operating characteristic (ROC) curve of 0.787. Results showed that 3.7% of nurses were free of PTSD, while 27.4%, 48.5%, and 18.6% of nurses had high levels of emotional exhaustion (EE), depersonalization (DP), and diminished personal accomplishment (PA), respectively. Nurses aged 30–40, single, married without children, non-regular employees, with less than three years of service, or working in the ICU had higher PTSD levels. The prevalence of burnout was higher among nurses working in general hospitals with many night shifts, especially those with severe PTSD. The nomogram was validated internally and showed good predictive performance. The study highlights the importance of addressing burnout among nurses during public health emergencies, particularly for single nurses working in general hospitals with many night shifts. Hospitals are advised to establish personal health records for nurses and implement support interventions to alleviate occupational stress. The study also identifies key risk factors for burnout, including the number of night shifts, type of hospital, marital status, and severity of PTSD. The findings emphasize the need for targeted interventions to reduce burnout and improve the mental health of nurses. The study has limitations, including the use of self-report methods and the inability to establish causal relationships. Further research is needed to explore the long-term effects of burnout and the effectiveness of interventions.This study investigates the risk and prediction of job burnout among nurses responding to public health emergencies. A cross-sectional survey was conducted in Xiangyang City, China, involving 1584 working nurses. The Impact of Events Scale-Revised (IES-R) and the Chinese version of the Maslach Burnout Inventory-General Survey (MBI-GS) were used to assess PTSD and burnout levels. Logistic regression analysis identified risk factors, and a nomogram was developed to predict burnout risk. The nomogram demonstrated good calibration and discrimination, with an area under the receiver operating characteristic (ROC) curve of 0.787. Results showed that 3.7% of nurses were free of PTSD, while 27.4%, 48.5%, and 18.6% of nurses had high levels of emotional exhaustion (EE), depersonalization (DP), and diminished personal accomplishment (PA), respectively. Nurses aged 30–40, single, married without children, non-regular employees, with less than three years of service, or working in the ICU had higher PTSD levels. The prevalence of burnout was higher among nurses working in general hospitals with many night shifts, especially those with severe PTSD. The nomogram was validated internally and showed good predictive performance. The study highlights the importance of addressing burnout among nurses during public health emergencies, particularly for single nurses working in general hospitals with many night shifts. Hospitals are advised to establish personal health records for nurses and implement support interventions to alleviate occupational stress. The study also identifies key risk factors for burnout, including the number of night shifts, type of hospital, marital status, and severity of PTSD. The findings emphasize the need for targeted interventions to reduce burnout and improve the mental health of nurses. The study has limitations, including the use of self-report methods and the inability to establish causal relationships. Further research is needed to explore the long-term effects of burnout and the effectiveness of interventions.