The expansion of big data, an essential factor in fostering the growth of the financial sector and the economy, is industrial agglomeration, and at the same time, it will also have a certain impact on the urban environment. Therefore, it is necessary to construct the IPAT model to study industrial agglomeration and urban environmental pollution in the era of big data, but the current IPAT model still has the problems of low industrial agglomeration, poor industrial technology level, and high industrial wastewater discharge. To better promote industrial agglomeration and reduce urban environmental pollution, this article aims to construct an IPAT model to more thoroughly analyze the mechanisms affecting environmental pollution in terms of urbanization level and industrial agglomeration. The materials and procedures of the study are first presented in the paper, which also examines the mechanisms of environmental pollution brought about by urbanization and industrial agglomeration. Finally, the traditional IPAT model is introduced, with industrial agglomeration variables added to optimize it. To analyze the practical application of the model in the research of industrial agglomeration and urban environmental pollution, this paper concludes by comparing it with the traditional model. The average test result of the industrial agglomeration model in this study is 38.14%, while the average test result of the classic model is 42.14%, according to the results. The model used in this study has an average test result of 63.24% for the industrial technology level, while the traditional model has an average test result of 57.87%. The model used in this work has an average test result for industrial wastewater discharge of 72,936,400 tons, while the traditional model has an average test result of 78,241,200 tons. This study validates the efficacy of the model in examining the relationship between urban environmental pollution and industrial agglomeration, and it also offers further insights for future in-depth research on the subject.