Previous studies have shown that many factors including body habitus, sex, and age of the patient, as well as contrast injection protocol contribute to the variability in contrast-enhanced cross-sectional imaging (i.e., CT). We have previously developed a compartmentalized differential-equation physiology-based pharmacokinetics (PBPK) model incorporated into computational human models (XCAT) to estimate contrast concentration and CT number (HU) enhancement of organs over time. While input to the PBPK model requires certain attributes (height, weight, age, and sex), this still results in a generic prediction as it only cohorts patients into 4 groups. In addition, it does not account for scanning parameters which influence the quality of the image. The PBPK model also requires an estimate of patient’s major organ volumes, not readily-available before a scan, which limits its potential application in prospective personalization of contrast-enhanced protocols. To address these limitations, this study used a machine learning approach to prospectively model contrast dynamics for an organ of interest (liver), given the patient attributes, contrast administration, and imaging parameters. To evaluate its accuracy, we compared the proposed model against the PBPK model. A library of 170 clinical images, with their corresponding patient attributes and contrast and imaging protocols, was used to build the network. The developed network used 70% of the cases for training and validation and the rest for testing. The results indicated a more accurate predictive performance (higher R2), as compared to the PBPK model, in estimating hepatic HU values using patient attributes, scanning parameters, and contrast administration.