The limited number of nitrogen dioxide (NO₂) surface measurements calls for the development of highly accurate approaches to estimating surface NO₂ concentrations. In this study, we leverage a new satellite instrument, the TROPOspheric Monitoring Instrument (TROPOMI), along with other predictor variables, to estimate daily surface NO₂ concentrations over Texas in 2019. We use the deep convolutional neural network (Deep‐CNN), an advanced deep learning algorithm, to obtain estimates and achieve a correlation coefficient (R) of 0.91, an index of agreement (IOA) of 0.95, and a mean absolute bias (MAB) of 1.75 ppb in surface NO₂ estimation. Additionally, we leverage a novel approach, shapley additive explanations (SHAP), to describe how Deep‐CNN understands each predictor variable. The SHAP results show that the Deep‐CNN model has an advanced understanding of the data set, revealing that TROPOMI closely captures levels of NO₂. In addition, we show the superiority of our Deep‐CNN model at estimating surface NO₂ over other well‐known machine learning and regression models in the field, including the support vector machines (SVM), random forest (RF), and multiple linear regression (MLR). Although SVM and RF show strong capabilities at estimating surface NO₂ concentrations, their accuracy is inferior to that of the Deep‐CNN model, ranking second and third in model accuracy in this study. The MLR, however, shows a poor ability at NO₂ estimation and ranks last among all models. Testing the impact of sample size on model performance, we also show that, compared to other models, Deep‐CNN needs more samples to trigger its strength at surface NO₂ estimation.