Abstract: Agricultural machinery is one of the most significant sources of non-road mobile emissions in recent years. Yet accurate estimation of its carbon and pollutant emissions is often hindered by the fuel consumption data and localized emission factors. In this study, a prediction framework was developed for tractor fuel consumption, CO₂, and NOx emissions from tractors. Massive BeiDou trajectory data were also integrated with portable emission measurement system (PEMS) measurements and the national operational emission inventory. 1) The samples were incorporated with complete fields from the BeiDou trajectory data and multidimensional features, such as the tractor speed, direction, engine speed, and torque. A feature set was constructed to predict the fuel consumption. A feature engineering process was conducted on the dataset of trajectory samples with complete fields from the 2023 summer harvest. The trajectories were segmented into kinematic fragments with a time step of 100s. A Residual Deep Neural Network (ResDNN) model with a 21-layer structure was employed to predict fuel consumption, with an R² of 0.75 and a MAPE of 24.46%. Fuel consumption features were further constructed for the missing feature fields, according to the operational duration and distance. Various algorithms of machine learning were used for the predictive models. Light Gradient Boosting Machine (LightGBM) model performed best (R²=0.77, MAPE=39.28%). 2) Localized emission models were established. PEMS tests were then conducted on four typical China IV standard tractors with the rated powers of 51.5, 58.8, 88.2, and 177 kW under varying engine loads. According to the experimental data, machine learning algorithms (ridge regression, random forest, decision tree, SVM, and LightGBM) were compared to construct emission prediction models using multi-dimensional input features. The evaluation results indicated that the ResDNN model achieved the best performance for fuel consumption prediction with the features (R²=0.75, and MAPE=24.46%), effectively resolving the gradient degradation in deep networks. In the simplified feature set, the LightGBM model performed optimally (R²=0.77, and MAPE=39.28%). Support Vector Machine was identified as the optimal model for CO₂ emissions (R²=0.95, MAPE=5.98%) prediction, while LightGBM performed best for NOx emissions (R²=0.86, MAPE=13.78%). Importance analysis revealed that instantaneous fuel consumption and engine speed were the most critical determinants for CO₂ emission, whereas environmental parameters shared a higher contribution to NOx prediction. Finally, these models were applied to the trajectory data of 870,192 tractors equipped with BeiDou terminals during the 2023 summer harvest (May 25 to June 25). A high-resolution national emission inventory was estimated after application. The total fuel consumption was 47.01×106 L, thus generating 128.21×103t of CO2 and 281.9t of NOx. The average daily emissions per tractor were 147.33 kg for CO₂ and 0.32 kg for NOx. Spatially, the emissions were concentrated in major agricultural provinces, such as Jiangsu, Henan, and Heilongjiang. Temporally, the emission peaks coincided perfectly with the busy farming rhythm. This finding can provide support data for carbon and pollutant emissions from large-scale agricultural machinery in green agriculture.