Deep learning models based on medical imaging enable numerical functional predictions in combination with regression methods. In this study, we evaluate the prediction performance of a deep learning-based model for the raw value and percent predicted forced expiratory volume in one second (FEV1) in patients with chronic obstructive pulmonary disease (COPD). To this end, ResNet50-based regression prediction models were constructed for FEV1 and %FEV1 based on 200 CT scans. 10-fold cross-validation was performed to yield ten models in aggregate. The prediction model for %FEV1 was externally validated using 20 data points. Two hundred internal CT datasets were assessed using commercial software, producing a regression model predicting airway [%WA] and parenchymal indices [%LAV]. The average Root Mean Squared Error(RMSE) value of the 10 predictive models was 627.65 for FEV1 as per internal validation and 15.34 for %FEV1. The externally validated RMSE for %FEV1 was 11.52, whereas that for %FEV1 was 23.18. The predictive model for %FEV1 yielded significant positive correlations corresponding to both internal and external validation. The proposed models exhibited better prediction accuracy for %FEV1 than for FEV1. Further studies are required to improve the accuracy further and determine the validity of longitudinal applications.