Purpose <p>ML techniques are powerful and novel approaches in modeling fluid bed drying of pharmaceutical granules. The aim of this study was to develop a prediction model and identify relative important factors in the evaluation of moisture content of pharmaceutical granules using ANN and SVM techniques for the datasets of APF. </p> Methods <p>ANN and SVM models were developed and compared, utilizing matlab 16.0a as a software tool. Optimizations of the models were also conducted applying GDR and improved TLCO techniques for FFNN and epsilon-SVR, respectively. The performance of the models was evaluated using a quantitative error metric: MAE, MSE, and R².</p> Results <p>This study reveals that the FFNN model is an optimal model for predicting moisture content of pharmaceutical granules for the TSG-FBD process model for the datasets of APF.</p> Conclusions <p>The model of FFNN, with MSE of 0.0009 and R² of 0.987, is built and accepted as an optimal model for predicting the moisture content of pharmaceutical granules. <i>Temperature, inlet airflow-rate, initial moisture, drying time, and screw speed</i>, respectively are the most important factors in determining the moisture content of the granules.</p>