This study proposes a novel method for the rapid detection of compost moisture content. The effects of the test frequency (1 to 100 kHz), compost moisture content (5% to 35%), temperature (25 to 65 °C), and bulk density (665.6 to 874.3 kg/m³) on the dielectric properties (the dielectric constant ${\epsilon }^{\prime }$ and the loss factor ${\epsilon }^{″}$) in the compost consisting of fresh sheep and manure corn were investigated. The mechanism for the change in dielectric properties was analyzed. The feature variables of dielectric parameters (${\epsilon }^{\prime }$, ${\epsilon }^{″}$, and the combination of ${\epsilon }^{\prime }$ and ${\epsilon }^{″}$) were selected using principal component analysis (PCA), and the selected characteristic variables and the full-frequency variables were used to perform support vector machine regression (SVR) modeling. The results revealed that the increase in both temperature and bulk density in the frequency band from 1 to 100 kHz increased ${\epsilon }^{\prime }$ and ${\epsilon }^{″}$. The PCA–SVR model with both ${\epsilon }^{\prime }$ and ${\epsilon }^{″}$ combined variables achieved the best results, with a prediction set coefficient of determination of 0.9877 and a root mean square error of 0.0026. In conclusion, the method of predicting the moisture content based on the dielectric properties of compost is feasible.