In this study, a data-driven regional carbon emissions prediction model is proposed. The Grubbs criterion is used to eliminate the gross error data in carbon emissions sensor data. Then, according to the nearby valid data, the exponential smoothing method is used to interpolate the missing values to generate the continuous sequence for model training. Finally, the GRU network, which is a deep learning method, is used to process these sequential standardized data to obtain the prediction model. In this paper, the wireless carbon sensor network monitoring data set from August 2012 to April 2014 trained and evaluated the prediction model, and compared with the prediction model based on BP network. The experimental results prove the feasibility of the research method and related technical approaches, and the accuracy of the prediction model, which provides a method basis for the nowcasting of carbon emissions and other greenhouse gas environmental data.