This paper examines the sustainable management of sports events through intelligent systems and economic strategies using Structural Equation Modeling (SEM) and the Analytical Hierarchy Process (AHP) from a cross-sectional dataset covering major international sports events from 2020 to 2024 to assess the effectiveness of data-driven decisionmaking in optimizing event sustainability. The research evaluates the effectiveness of SEM- and AHP-based decision models in sports event planning and sustainability metrics, and their implications on economic feasibility and environmental responsibility.To carry out a comparative assessment of data-driven event management approaches with the SEM and AHP framework, a multi-criteria evaluation model was developed, which assists in quantifying sustainability indicators and in representing stakeholder-driven performance metrics offered in existing strategic frameworks. The results reveal that the integration of AI-driven decisionmaking promotes cost efficiency and environmental sustainability; however, differences exist in its impact on economic viability, social engagement, and governance structures. Finally, based on empirical findings, suggestions are proposed from economic, technological, and regulatory perspectives, emphasizing data optimization techniques and sustainable policy implementation, in order to enhance resilience and long-term viability of sports events. The recommendations emphasize the need for adaptive strategies of intelligent systems integration, stressing stakeholder collaboration, policy alignment, and continuous refinement of sustainability models.
Usmanova et al. (2025) studied this question.