This paper introduces a machine learning framework that can generate small-signal equivalent circuit models for AlGaN/GaN high electron mobility transistors (HEMTs). The framework uses an artificial neural network to predict the device's admittance parameters over a wide frequency range. This model can efficiently explore the design space and conduct parametric sweeps, allowing for quick assessment of variability in various radio-frequency performance metrics without relying on time-consuming TCAD simulations.