In the domain of big data geographic screening for environmental pollutants, the expeditious dissemination of testing results to environmental investigation professionals is pivotal in facilitating comprehensive analysis and the implementation of more efficacious strategies for managing environmental issues. However, this endeavor can prove to be particularly arduous when conducting examinations in remote, resource-scarce rural areas and field environments, where deficient infrastructure often emerges as the principal impediment to unimpeded environmental monitoring. Therefore, the development of a reliable and portable monitoring strategy with the ability to analyze large amounts of data is highly required. Here, a deep-learning (DL)-assisted portable sensing strategy was developed based on thermal and pH dual-responsive nano-structural superwetting surfaces, for highly reliable, quick, and field monitoring of environmental pollutants. In our experiment, bisphenol A (BPA) was selected as the representative pollute. The achieved limit of detection, attaining a remarkably low value of 1.05 μM, unequivocally adhered to stringent international testing standards for evaluating the migration of BPA in thermal paper. Based on a DL image classification algorithm, highly precise predictions regarding the migration of BPA concentration were achieved, with an accuracy rate exceeding 99%. Furthermore, it successfully facilitated automated and exceedingly reliable monitoring of the migration of BPA from thermal paper within the principal provinces of thermal paper production in China. This strategy engenders the potential to establish correlations between environmental pollutant concentrations in specific regions and the prevalence of certain human ailments.