In the context of increasing water-energy scarcity in Central Asian countries, the development of innovative resource management approaches based on advanced digital technologies has become particularly relevant. This study explores the application of Big Data, artificial intelligence (AI), and predictive analytics to enhance the efficiency of water-energy resource management in the region. The paper proposes a comprehensive integrated management model that incorporates multidimensional data on hydrological processes, climate change, energy demand, and transboundary resource distribution. The methodological framework includes machine learning, neural networks, optimization algorithms, and scenario analysis. Special attention is given to the development of digital twins of hydropower systems and the forecasting of water flows. The results demonstrate a significant improvement in forecasting accuracy and resource allocation optimization, contributing to the reduction of conflict potential among countries in the region and enhancing the sustainability of energy systems. The proposed model can serve as a decision-support tool at both national and transnational levels. One of the most significant outcomes of this study is a reduction in the conflict index by more than 50%, indicating that the proposed model effectively aligns the interests of regional stakeholders. While traditional management systems are typically oriented toward national priorities-often leading to conflicts between upstream and downstream countries-the proposed approach adopts a regional perspective, enabling the identification of compromise solutions. Thus, the implementation of intelligent management systems can be considered not only a technological solution but also a mechanism for enhancing institutional stability and economic resilience in Central Asia.