This study proposes a method based on a convolutional neural network (CNN) and support vector regression machine (SVR) for predicting the vertical distribution of NO₂ in the troposphere by multi-axis differential optical absorption spectroscopy (MAX-DOAS) technology. Taking the Nanjing site as an example, we obtain the O₄ and NO₂ differential slant column density (dSCD) according to the raw MAX-DOAS data collected by QDOAS fitting in 2019, invert the tropospheric NO₂ profile by combining the optimal estimation-based aerosol and trace gas profile inversion algorithm-PriAM, and use the profile as the output of the prediction model. In addition, the input variables of the prediction model are selected by the mean impact value method, with MAX-DOAS data, temperature, aerosol optical thickness, and low cloud coverage finally identified as the optimal input variables for the model. Furthermore, the network structure and parameters are optimized through experiments, and the average percentage error of the final CNN-SVR prediction model in the test set with PriAM is only 9.14%, which is 8.22%, 6.00%, and 32.28% lower than that of the separately constructed CNN, SVR, and backpropagation models, respectively. Therefore, CNN-SVR can effectively predict tropospheric NO₂ profiles by using MAX-DOAS data.