In this study, we collected published data on 460 drugs with moderate diversity in chemical structures, Fa, and dose amounts in humans. To determine the most accurate Fa prediction method, we combined mechanism-based and data-driven methods. The novel ML method, which incorporated both structural descriptors and GUTFW parameters, demonstrated a higher predictivity than that of the GUTFW and conventional ML models for the 10-fold CV and test datasets. Hence, we developed a more accurate prediction method for human Fa using a combination of data-driven ML and a mechanism-based GUTFW that does not require any experimental data using the RF algorithm. This model is expected to have applications in the early drug discovery stage because it does not require experimental data, whereas the GUTFW prediction appears to be suitable for the late drug development stage because it requires accurate experimental data. Furthermore, the important substructures calculated in the Chemprop framework partially agree with the knowledge and experience of medicinal chemists.