Reliability of Gallium Nitride (GaN) power semiconductor transistors are extremely important to uncover its immense possibility in high power conversion applications. Accurate remaining useful life (RUL) estimation ensures robust operational reliability of GaN FET in high power applications. Fault precursor trajectory based RUL estimation methods results in large estimation error due to both hybrid tendency of the trajectories and presence of measurement noise. Tracking and projecting fault precursor trajectory for RUL estimation is another crucial challenge when the system condition changes suddenly. In this paper, an epsilon support vector regression ( ε-SVR) aided auxiliary particle filter (APF) is applied for RUL estimation of GaN field-effect transistor (FET). On-state resistance (RDS,ON) is used as the fault precursor for this study. RDS,ON of GaN FETs show dynamic characteristics during switching operation and results in false alarm in degradation identification. However, Steady state RDS,ON shows strong relationship with degradation. In this paper, a signal conditioning circuit is used to acquire RDS,ON with proper resolution. ε-SVR-assisted-APF also accurately estimates the RUL when operating condition changes suddenly. The failure region of GaN FET is modeled which shows the failure probability after RDS, ON reaches the threshold value. The effectiveness of ε-SVR-assisted-APF is validated using accelerated ageing data under power cycling test.