Accurate CO₂ emission measurement and dynamic forecasting are crucial for achieving China’s “dual carbon” goals (carbon peak and carbon neutrality). This study integrates the emission factor method, CO₂-CEMS and machine learning technologies to propose a CO₂ emission measurement and forecasting method based on multi-source data fusion, for the purpose of providing an efficient and accurate carbon emission monitoring tool for coal-fired power plants. In the investigation, by comparing the CO₂ emission calculation results obtained by different methods, the performances of different machine learning algorithms are evaluated, and a dynamic forecasting model based on multi-source data is developed. Experimental results in Units 1 and 3 of a power plant of Guoneng Group Co., Ltd. in Hebei, China show that the relative deviations of CO₂ emission calculations between the emission factor method and CO₂-CEMS for the two units are 1.63% and -1.27%, respectively, meaning that the two methods are of good cross-validation. By comparing the performance of various machine learning models (such as XGBoost, LightGBM, and AdaBoost), beyond the two conventional evaluation metrics, namely the determination coefficient (R²) and the mean absolute percentage error (MAPE), a new selection criterion, namely the mean deviation (x_c), is proposed by applying trained models to other units. Then, x_c is used to assess the generalization capability of machine learning algorithms for further model screening. The results reveal that AdaBoost exhibits superior performance in prediction accuracy and stability, along with higher generalization capability and robustness. The dynamic CO₂ emission forecasting using the optimized AdaBoost algorithm achieves R² values greater than 0.99 on both the training and the testing sets, with a MAPE below 2%, which indicates that the algorithm is of high prediction accuracy, stability, generalization ability and robustness. The proposed multi-source data fusion method not only effectively overcomes the limitations of traditional methods in dynamic scenarios but also enables precise hourly CO₂ emission forecasting based on real-time data.