The rapid spread of COVID-19 is likely to result in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7,772 (75.9%) recovered, and 2,237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age TRANS > 70, male TRANS sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus MESHD diabetes mellitus HP (DM), chronic lung disease HP lung disease MESHD, or asthma MESHD asthma HP were significantly associated with increased risk of mortality (p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities SERO (90.3% [95% confidence interval: 83.3, 97.3]and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities >90%. The most significant predictors for LASSO included old age TRANS and preexisting DM or cancer; for RF they were old age TRANS, infection MESHD route (at large clusters or from personal contact with an infected individual), and underlying hypertension MESHD hypertension HP. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources have to be wisely allocated without hesitation.