DNA methylation is closely related to senescence, so it has been used to develop statistical models, called clock models, to predict chronological ages accurately. However, because the training data always have a biased age distribution, the model performance becomes weak for the samples with a small age distribution density. To solve this problem, we developed the R package eClock, which uses a bagging-SMOTE method to adjust the biased distribution and predict age with an ensemble model. Moreover, it also provides a bootstrapped model based on bagging only and a traditional clock model. The performance on three datasets showed that the bagging-SMOTE model significantly improved rare sample age prediction. In addition to model construction, the package also provides other functions such as data visualization and methylation feature conversion to facilitate the research in relevant areas.