Background: COVID-19 can cause acute respiratory distress syndrome (ARDS), and artificial intelligence (AI) algorithms for chest X-ray (CXR) analysis have been developed to assess pulmonary damage in COVID-19 patients. We evaluated the combination of AI-CXR and hematological parameters to predict mortality and intubation events for COVID-19 patients. Methods: This study is a retrospective cohort study of COVID-19 patients for which we have collected data during the 2020–2022 period at Airlangga University Hospital, Surabaya, Indonesia. Results: The total number of patients involved in this study was 312. The results of the scoring evaluation using a combination of AI-CXR, hematology parameters, respiratory rate (RR), and SaO2 showed ROC 0.854; p = 0.000; sensitivity: 74.2%; specificity: 89% in predicting 30-day mortality events, and ROC 0.846; p = 0.000; sensitivity: 80.3%; specificity: 80.9% in predicting 30-day intubation events. Meanwhile, without using AI-CXR, it shows ROC 0.822; p = 0.000; sensitivity: 80.3%; specificity: 81.7% in predicting 30-day mortality events, and ROC 0.815; p = 0.000; sensitivity: 77.3%; specificity: 81.7% in predicting 30-day intubation events. Conclusion: The combination of hematology parameters with or without AI-CXR has excellent sensitivity and specificity in predicting the incidence of mortality and intubation in COVID-19 patients. Clinical trial: Not applicable. © The Author(s) 2026.