Gears are widely used in machines to transmit torque from one shaft to another shaft and to change the speed of a power source. Gear failure is one of the major causes for mechanical transmission system breakdown. Therefore, early gear faults must be immediately detected prior to its failure. Once early gear faults are diagnosed, gear remaining useful life (RUL) should be estimated to prevent any unexpected gear failure. In this paper, an intelligent prognostic system is developed for gear performance degradation assessment and RUL estimation. For gear performance degradation assessment, which aims to monitor current gear health condition, first, the frequency spectrum of gear acceleration error signal is mathematically analyzed to design a high-order complex Comblet for extracting gear fault related signatures. Then, two health indicators called heath indicator 1 and health indicator 2 are constructed to detect early gear faults and assess gear performance degradation, respectively, using two individual dynamic Bayesian networks. For gear RUL estimation, which aims to predict future gear health condition, a general sequential Monte Carlo algorithm is applied to iteratively infer gear failure probability density function (FPDF), which is used to predict gear residual lifetime. One case study is investigated to illustrate how the developed prognostic system works. The vibration data collected from a gearbox accelerated life test are used in this paper, where the gearbox started from a brand-new state, and ran until gear tooth failure. The results show that the developed prognostic system is able to detect early gear faults, track gear performance degradation, and predict gear RUL.