Abstract
As a tool for processing and analyzing multiple types of data in various fields, GIS is of significant importance and contributes greatly to the digitization, automation, and management of diverse processes. The functionalities of these systems can be enhanced with the capabilities of artificial intelligence (AI) for improving analysis algorithms. Their combined use, based on machine learning aimed at discovering features and generating solutions unnoticed by human factors, can significantly improve the quality of research and the results obtained. This is especially true in cases where large volumes of data in the form of time series need to be handled. This article analyzes the potential for the productive use of GIS and AI in the field of air pollution research and forecasting, based on available data on the distribution of different types of pollutants and climatic data for urbanized areas. The generated digital models through the functionalities of GIS are supplemented by using AI for time series analysis. The results of the study will be used in future developments aimed at using AI and GIS to study pollen pollution in urban conditions and reduce health risks.