ECONOMIC AND MATHEMATICAL MODELING OF SUSTAINABLE AGRICULTURAL DEVELOPMENT USING ARTIFICIAL INTELLIGENCE

Esenbek Belek uulu, Ainura Dyikanova, Turgunbek Zhumaliev, Samyibek Begaliev, Rakhat Sagyndykova, Tologon Kerimov
Received: 11.01.2026
Revised: 05.06.2026
Accepted: 01.07.2026
Pages: 169 –193
Retrieved from: Volume 4, No. 2, 2026
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Abstract

This article is devoted to the study of economic and mathematical modeling methods for sustainable development of the agro-industrial complex using artificial intelligence technologies. In the context of global climate change and the growing need to ensure food security, traditional planning methods require enhancement through adaptive algorithms. The study examines the application of neural networks and deep learning techniques for forecasting the state of agricultural systems. Particular attention is paid to the integration of economic, environmental, and social indicators within a unified modeling framework. It is demonstrated that the use of hybrid technologies combining statistical analysis and artificial intelligence significantly improves forecasting accuracy and the effectiveness of managerial decision-making. The results of the study can be applied to optimize agricultural production and to develop long-term sustainable development strategies for regions.

Keywords

sustainable development, artificial intelligence, mathematical modeling, neural networks, agro-industrial complex, climate-smart agriculture

Suggested citation

Belek uulu, E., Dyikanova, А., Zhumaliev, T., Begaliev, S., Sagyndykova, R., & Kerimov, Т. (2026). ECONOMIC AND MATHEMATICAL MODELING OF SUSTAINABLE AGRICULTURAL DEVELOPMENT USING ARTIFICIAL INTELLIGENCE. Global Scholars’ Space, 4(2), 169-193.

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