Net ecosystem exchange (NEE) serves as an important indicator for assessing how carbon dioxide (CO2) flows between the land surface and the atmosphere, particularly in the context of climate change. Although in situ instruments are available for monitoring carbon exchanges, but the temporal and spatial scale limitations of these observations’ attempts have been made to develop models for NEE prediction. This study examined daily NEE variations across four plant functional types (PFTs): Deciduous broadleaf forest (DBF), Coniferous forest (ENF), Mixed Forest (MF), and Grassland (GRA). Using the, a machine learning approach, namely random forest (RF) method. Four meteorological variables including solar radiation, air temperature, soil temperature, and relative humidity were used as inputs for the model. The measured and predicted values were evaluated using four statistical indices of R², NSE, Bias, and RMSE. The results indicated that DBF had the most accurate modeling performance, whereas GRA had the least. An analysis of the relative significance of environmental variables revealed that solar radiation was the most important and relative humidity the least important across all PFTs. Additionally, soil temperature improved the model performance in grasslands more significantly compared to air temperature, unlike the results in forest functional types. The study also highlighted that uncertainty remains a significant issue during different phenological stages across all PFTs; with the highest uncertainty between days 140-220 in forest types and 120-210 in grassland, according to the Julian calendar.