Rationale: Gas exchange abnormalities in Sickle Cell Disease (SCD) may represent cardiopulmonary deterioration. Identifying predictors of these abnormalities in children with SCD (C-SCD) may help us understand disease progression and develop informed management decisions. Objectives: To identify pulmonary function tests (PFT) and biomarkers of systemic disease severity that are associated with and predict abnormal carbon monoxide diffusing capacity (DLCO) in C-SCD. Methods: We obtained PFT data from 51 C-SCD (115 observations) and 22 controls, and identified predictors of DLCO for further analyses. We formulated a rank list of DLCO predictors based on machine learning algorithms (XGBoost) or linear mixed-effect models and compared estimated DLCO to the measured values. Finally, we evaluated the association between measured and estimated DLCO and clinical outcomes, including SCD crises, pulmonary hypertension, and nocturnal hypoxemia. Results: DLCO and several PFT indices were diminished in C-SCD compared to controls. Both statistical approaches ranked FVC%, neutrophils(%), and FEV25%-75% as the top three predictors of DLCO. XGBoost had superior performance compared to the linear model. Both measured and estimated DLCO demonstrated significant association with SCD severity indicators. DLCO estimated by XGBoost was associated with SCD crises (beta=-0.084 [95%CI -0.134, -0.033]) and with TRJV (beta=-0.009 [-0.017, -0.001]), but not with nocturnal hypoxia (p=0.121). Conclusions: In this cohort of C-CSD, DLCO was associated with PFT estimates representing restrictive lung disease (FVC%), airflow obstruction (FEV25%-75%), and inflammation (neutrophil%). We were able to use these indices to estimate DLCO, and show association with disease outcomes, underscoring the prediction models’ clinical relevance.