Extracorporeal membrane oxygenation (ECMO) provides critical cardiac support, but predicting outcomes remains a challenge. We enrolled 1,748 adult venoarterial (VA)-ECMO patients at the National Taiwan University Hospital between 2010 and 2021. The overall mortality rate was 68.2%. Machine learning with the random survival forest (RSF) model demonstrated superior prediction for in-hospital mortality (area under the curve [AUC]: 0.953, 95% confidence interval (CI): 0.925–0.981), outperforming the Sequential Organ Failure Assessment (SOFA; 0.753 [0.689–0.817]), Acute Physiology and Chronic Health Evaluation (APACHE) II (0.737 [0.672–0.802]), Survival after Venoarterial ECMO (SAVE; 0.624 [0.551–0.697]), ENCOURAGE (0.675 [0.606–0.743]), and Simplified Acute Physiology Score (SAPS) III (0.604 [0.533–0.675]) scores. Failure to achieve 25% clearance at 8 hours and 50% at 16 hours significantly increased mortality risk (HR: 1.65, 95% CI: 1.27–2.14, p < 0.001; HR: 1.25, 95% CI: 1.02–1.54, p = 0.035). Based on the RSF-derived variable importance, the RESCUE-24 Score was developed, assigning points for lactic acid clearance (10 for <50% at 16 hours, 6 for <25% at 8 hours), SvO₂ <75% (3 points), oliguria <500 ml (2 points), and age ≥60 years (2 points). Patients were classified into low risk (0–2), medium risk (3–20), and high risk (≥21). The medium- and high-risk groups exhibited significantly higher in-hospital mortality compared with the low-risk group (HR: 1.93 [1.46–2.55] and 5.47 [4.07–7.35], p < 0.002, respectively). Kaplan–Meier analysis confirmed that improved lactic acid clearance at 8 and 16 hours was associated with better survival (log-rank p < 0.001). The three groups of the RESCUE-24 Score also showed significant survival differences (log-rank p < 0.001). In conclusion, machine learning can help identify high-risk populations for tailored management. Achieving optimal lactic acid clearance within 24 hours is crucial for improving survival outcomes.