This study examines the risk of a public debt crisis in Vietnam by applying a binary decision tree framework to identify key determinants and critical threshold conditions that may increase fiscal vulnerability. In the aftermath of the COVID-19 pandemic and amid heightened global economic uncertainty, assessing debt sustainability requires more than evaluating absolute debt levels; it also necessitates consideration of macroeconomic conditions that could trigger heightened risk. Using data for the period 2019-2023, the analysis focuses on the public debt-to-GDP ratio along with selected macroeconomic indicators. A binary decision tree model is developed to classify risk states and detect significant splitting thresholds within the dataset. The findings indicate that although Vietnam’s public debt-to-GDP ratio has declined and remains below the statutory ceiling of 60 percent of GDP, fiscal risk exposure continues to be influenced by economic growth dynamics and related fiscal factors. The model helps identify specific conditions under which the probability of elevated risk increases substantially. The study contributes empirical evidence on the application of non-linear classification techniques in public debt risk assessment and offers policy-relevant insights for the development of early warning mechanisms aimed at strengthening long-term fiscal sustainability.