The study constructed and validated a machine learning-based predictive model for the clinical efficacy of bronchoalveolar lavage (BAL) in 206 patients with severe community-acquired pneumonia. The patients were divided into a training set (n=144) and a validation set (n=62) in a ratio of 7:3. Independent risk factors for low efficacy were age ≥60 years, chronic obstructive pulmonary disease, procalcitonin ≥2 ng/mL, C-reactive protein ≥100 mg/L, PaO₂ <60 mmHg, and PaCO₂ ≥50 mmHg (all p<0.05). The Random Forest (RF) model achieved the best AUC values of 0.799 in the training set and 0.778 in the validation set, outperforming the KNN and GB models. In the training set, 32 patients (22.22%) and in the validation set 13 patients (20.97%) had poor efficacy. The RF model effectively identifies key factors influencing BAL outcomes. The study is single-center and requires multicenter validation.