In this study, we investigated the potential of using decision tree Machine Learning (ML) algorithm and profiles of vegetation and moisture indices extracted from Land Remote-Sensing Satellite (System, Landsat) time series to identify the ages of rubber plantations in Thalang district, Phuket province, southern Thailand. The secondary Land Use and Land Cover (LULC) data and historical imagery from Google Earth^TM were used to distinguish plantation boundary and the establishment year of rubber plantation (T₀). The inter-annual profiles of spectral indices for each rubber plantation were obtained from 129 Landsat images (summer period from October 1991 to April 2018). The predictors were generated from summary distribution values of spectral indices during summer, including their difference and ratio at two years before to six years T₀ for Recursive Partitioning (RP) supervised classification algorithm. Modelling dataset from ‘known T₀’ plantations was divided into the training and testing datasets with a 60:40 ratio. The training model was 30 times repeated training while cross-validation assessment was tested to optimize an appropriated hyperparameter based on F₁ score. Then, the best performance training model was applied to both modelling and predicting (‘unknown T₀’ plantations) datasets. The predicted T₀ for each plantation was selected based on results aggregation of 100 times repeated prediction. The results show that the RP model with a complexity parameter as 0.01 and the predictors generated only from Normalized Difference Vegetation Index (NDVI) profile acceptable achieves an accuracy of 84.4% and 84.7% for lowland and highland modelling datasets, respectively. The root means squared error (RMSE) of predicted establishment year were 0.83 years at plantation scale.