Forest age significantly affects trends of forest carbon sink. The spatial data of forest age are required to reduce uncertainties in regional and global forest carbon sinks. Forest age and canopy height are closely linked. In recent years， more and more high-resolution forest canopy height data derived from remote sensing available， making it possible to map forest age at a high-resolution. However， the feasibility of high-resolution mapping of temperate forest age from remotely sensed forest height. Therefore， studying the estimation and mapping of temperate forest age based on forest height remote sensing data is of great significance for improving the accuracy of regional carbon sink dynamics monitoring， optimizing forest management strategies， and deepening the understanding of carbon sequestration mechanisms in temperate forest ecosystems. With Heilongjiang Province as the study area， the optimal growth equations describing the changes of canopy height with forest age were determined for different forest types， including deciduous broadleaf， evergreen needleleaf， deciduous needleleaf， and mixed forests and the plot data were corrected for time， based on measurements at 1821 plots. 70% of plot data were randomly selected for model training and remaining 30% of data were used for model validation. With forest height derived from LiDAR data and environmental factors （including the length of the growing season， the highest monthly average temperature， and slope） as the independent variables， models estimating forest model were constructed using the Random Forest （RF）， the Support Vector Machine （SVM）， and the LightGBM methods， respectively. The best model was used to map forest age in 2020 at a spatial resolution of 30 and the characteristics of forest age in the study area was analyzed. The results showed that for the training and validation samples， the RF model achieved the highest R² （0.77） and lowest root mean square error （RMSE=10.20）， followed by LightGBM model. The SVM model achieved the lowest R² （0.63） and highest RMSE （11.85）. There were obvious spatial variations in the forest age estimated using the RF model. Forest age were significantly higher in Daxinganling district and Yichun city than in other regions， and were low in Heihe city. Deciduous coniferous forests had the highest average age， followed by evergreen coniferous forests and mixed forests. Deciduous broadleaf forests had the lowest average age. Forest ages in the study area averaged 73 years， with 75% of the forests aged between 40 and 100 years. Forests older than 100 years accounted for 17%， and 8% of the forests were younger than 40 years. The study shows that the age of temperate forests in China can be estimated using a machine learning method by combining remotely sensed forest height with environmental factors， which was valuable for mapping regional and global forest age at a high spatial resolution.