Electronic systems life is an essential aspect of ensuring reliability and safety. An accurate age estimation could assimilate, which is helpful for any electronics system. It would also positively impact the minimisation of electronics waste and support the endeavour of green computing. In this paper, we propose a methodology for age estimation using the Gaussian Process Regression (GPR) model. Our methodology requires an RO sensor, temperature sensor, and trained GPR model for the age prediction. The Ring Oscillator (RO) output frequency relies on the trackable path, temperature, voltage and ageing. These dependencies are utilized for the training of the GPR model. We exhibit the output frequency degradation of the ring oscillator through the Synopsys PrimeSim Hspice tool with the 32nm Predictive Technology Model (PTM). We consider variations from 0 °C to 100 °C in temperature and 0. 8V to 1. 05V in the voltage. Our methodology predicts age precisely, showing average prediction accuracy in 85.35% cases with a deviation of one month for 13-stage RO and 90.42% cases in 21-stage RO. Our proposed methodology is more accurate than the state-of-the-art techniques in terms of prediction accuracy as well as age estimation deviation. The prediction accuracy improvement got 9.59% for 13-stage and 9.17% for 21-stage RO on our dataset than the state-of-the-art technique with a month deviation, respectively, as opposed to 2.4 months for the state-of-the-art method. © 2022 IEEE.