Abstract
Tourist destinations such as Imantau-Shalkar, Borovoe, Altai, and Baikal, located across Kazakhstan and Russia, are increasingly struggling with infrastructure problems. One major reason is the lack of advanced digital systems that accurately forecast tourist flows. During peak seasons, inefficiencies in these areas often exceed 30%, leading to congestion, poor service delivery, and safety risks. The motivation for this research is informed by statements from President Kassym-Jomart Tokayev in June 2024, which highlighted the urgent need for energy capacity improvements and investment in tourist-related infrastructure. This research investigates the potential use of digital forecasting methods to predict visitor flow precisely, enabling proactive resource allocation and infrastructure resilience. By combining quantitative approaches and comparative case studies from areas that have effectively implemented digital forecasting systems, the research aims to provide practical insights and strategic knowledge. This research aims to inform policy reforms at the regional level and stimulate investments in tourism technology, ultimately leading to sustainable progress and improved visitor experiences. Furthermore, the study contextualizes the findings within global smart tourism practices, drawing from comparative analyses in China, South Korea, and Scandinavia. It highlights the regional research gap in Central Asia. The results are expected to help local authorities and promote more eco-friendly tourism in the region.