Patients supported by mechanical ventilation require frequent invasive blood gas samples to monitor and adjust the level of support. We developed a transparent and novel blood gas estimation model to provide continuous monitoring of blood pH and arterial CO2 in between gaps of blood draws, using only readily available noninvasive data sources in ventilated patients. The model was trained on a derivation dataset (1,883 patients, 12,344 samples) from a tertiary pediatric intensive care center, and tested on a validation dataset (286 patients, 4030 samples) from the same center obtained at a later time. The model uses pairwise non-linear interactions between predictors and provides point-estimates of blood gas pH and arterial CO2 along with a range of prediction uncertainty. The model predicted within Clinical Laboratory Improvement Amendments of 1988 (CLIA) acceptable blood gas machine equivalent in 74% of pH samples and 80% of PCO2 samples. Prediction uncertainty from the model improved estimation accuracy by 15% by identifying and abstaining on a minority of high-uncertainty samples. The proposed model estimates blood gas pH and CO2 accurately in a large percentage of samples. The model's abstention recommendation coupled with ranked display of top predictors for each estimation lends itself to real-time monitoring of gaps between blood draws, and the model may help users determine when a new blood draw is required and delay blood draws when not needed.