This study examined co-occurring patterns of mental health among disaster victims using latent profile analysis and assessed the difference between sociodemographic factors and protective factors that affect group classification. The data of 2300 disaster victims from 2019 (4th wave) NDMI (National Disaster Management Research Institute) for Long-term Survey on the Change of Life of Disaster Victims were analyzed. The latent profile analysis revealed that three profiles; High comorbid symptom (HCS) (6.2%), Medium comorbid symptom (MCS) (22.6%), and Low symptom (LS) (71.2%). The factors that explain the difference in this divided profile group were the type of disaster, hurt, income, age, elapsed years, resilience, and community resilience in the multinomial logistic regression. When individual resilience and community resilience are high, more effective in making people belong to the low comorbid symptom group. Therefore, there is a need for a strategy that promotes synergy between the two relationships while maintaining a dual focus point of view that fosters resilience at the individual and community level together.