Negative-Bias Temperature Instability (NBTI) poses serious threats to modern ICs and may lead to timing and functional failure. If these failures occur in industrial automated production systems, the malfunctioning system can cause significant economic losses due to unacceptable fabrication quality and yield. Although preventive maintenance is a useful way to avoid such a situation, executing preventive maintenance on a frequent basis will also introduce significant production line downtime. To accurately execute the preventive maintenance just before circuit failure occurs, a chip remaining lifetime estimation method is in demand. In this paper, we propose a framework for predicting the remaining lifetime of the chip. This framework can adapt to changes in the process and operating voltage. The framework tracks representative aging indicators through machine learning methods in order to predict the remaining lifetime of the chip. In addition, we also investigate the impact of changes in hyperparameters, such as training sample sizes, on prediction performance. The experimental results show that the proposed framework achieves an average accuracy and precision of 97.3% and 97.2%, respectively, and the accuracy is 2.54% higher than the strategy used to determine chip health level in a previous work.