Physiologically based pharmacokinetic (PBPK) modeling has the potential to estimate internal chemical exposures. Algorithms for predicting the input parameters for PBPK modeling, such as absorption rate constants (k_a), were previously reported for 323 chemicals in rats. In this study, a currently updated system for estimating the fraction absorbed × intestinal availability of compounds, along with a modified estimation system that generates k_a values, is reported, based on the previously analyzed 323 primary compounds, 10 secondary compounds, and 39 additional substances. The in silico estimation of input parameters for PBPK models (i.e., fraction absorbed × intestinal availability and k_a) was adapted for an updated panel of 372 chemicals using machine learning algorithms based on between 16 and 18 in silico-calculated chemical properties. Simplified human PBPK models were then used to calculate virtual areas under the plasma concentration–time curve (AUC) based on two sets of input parameters, i.e., traditionally derived values from in vivo data and those calculated in silico using the current updated machine learning systems. The AUC data sets were well correlated; the current correlation coefficient increased from 0.61 to 0.82 (p < 0.01, n = 372). Therefore, the above-described computational methods constitute a new alternative approach that could contribute to chemical safety evaluations.