This study aims to develop an early warning system for debt rescheduling in ASEAN countries by utilizing yearly time series data from 1999 to 2019. The logit model is employed to construct the early warning system for debt rescheduling in ASEAN countries, with debt rescheduled data collected from The World Bank’s International Debt Statistics database. The empirical results indicate that the early warning system model for debt rescheduling in ASEAN countries should comprise four variables: the unemployment rate, concessional debt to total debt, external debt over GDP, and international reserve to short-term debt. Interestingly, when setting the cutoff value at 0.5, the model demonstrates high predictive accuracy, with a Type II error rate of 10 percent and a Type I error rate of only 4.1 percent. Overall, the early warning system model for debt rescheduling in ASEAN countries appears capable of correctly predicting events 80 times out of 84.