The present study deals with the application of the supervised machine learning regression algorithms known as Linear Regression (LR), Support Vector Machine (SVM), and Gaussian process regression (GPR) to the frequency and temperature-dependent dielectric parameters of polymer/inorganic film composites. The frequency and temperature-dependent experimental data set of the dielectric parameters (ε^' and ε^'') of Polypyrrole/Kufeki Stone (PPy/KS) has been utilized. ML models were compared based on their model performance and the most suitable was chosen. After choosing the most suitable ML model, at first, the predictions of the same dielectric parameters of the same samples for different temperatures have been made. Then, the predictions of temperature and frequency-dependent ε^' and ε^'' have been performed for the new PPy based composites consisting of different KS additives that were not produced experimentally. As a result of machine learning, the saturation for KS reinforcing material weight % for dielectric parameters has been determined for capacitor applications. In the light of experimental data and the estimations made by the GPR algorithm, some specific KS additive percentage, working temperature, and frequency ranges have been suggested for the capacitor applications of PPy.