Demographic growth in Kazakhstan over the past decades has had a significant impact on the entire education system, particularly at the preschool and primary levels. High birth rates have led to an increasing number of children requiring enrollment in kindergartens and first-grade classes. This often results in a shortage of available places, increased workload for teaching staff, and a decline in the quality of educational services. This paper examines the application of Business Intelligence (BI) tools and Geographic Information Systems (GIS) for forecasting potential shortages of educational places and identifying regional priorities in infrastructure development. A predictive model is presented, based on birth rate indicators and age cohorts, which enables the estimation of future demand for preschool and primary school capacity across the regions of Kazakhstan. The study highlights the urgent need for proactive planning and targeted investment to prevent critical shortages and to ensure equitable access to quality education. The findings can serve as a foundation for the development of effective public education policies and support the formulation of regional strategies that reflect current demographic trends.

Balanced socio-demographic development is essential for regional resilience and sustainable growth. Forecasting social and demographic processes in the region based on the use of Business Intelligence systems allows for the consideration of a wide range of contradictory and diverse factors and the anticipation of potential scenarios and risks of development. Business Intelligence systems are used for the collection, analysis, and processing of large volumes of data to support decision-making in business, finance, logistics, public administration, and other fields. The goal of the study is to assess the effectiveness of using Business Intelligence systems and geographic information systems (GIS) to achieve balanced socio-demographic development in Kazakhstan. Key indicators include demographic metrics and social infrastructure. The methodology involves the use of analytical platforms such as Power BI and ArcGIS, along with predictive models for forecasting current and future demographic changes. The outcome of the study is a tool for calculating the load on the social infrastructure of Kazakhstan's regions, enabling informed management decisions at the regional level to ensure resilience and balanced socio-demographic growth. Conclusions: The study demonstrates that Business Intelligence systems, integrated with GIS, have significant potential for improving the accuracy of socio-demographic forecasting.