In this study, an accurate temperature prediction model is proposed for GaAs HBT, which considers both the bias voltage and current rather than power consumption only. The increase in temperature is closely related to the heat source property, which leads to a complex interaction between the lattice vibration and the uneven distribution of the electric field and current density. To improve the accuracy and stability of the temperature prediction model, a machine learning method of Extreme Learning Machine (ELM) optimized with an Atomic Search Algorithm (ASO) is introduced. The validity of the model is verified by comparing it with experimental observations by the QFI InfraScope TM temperature mapping system. The predicted temperatures for an 8 × 8 HBT power cell fabricated with 2 μm GaAs technology show good agreement with the measurement results, with a ±2 °C error and a relative error deviation below 3%. This demonstrates the superior performance of the proposed model in accurately predicting the temperature of GaAs HBT.