application of sustainable development principles. These applications require careful consideration of regional specificity, socio-economic context, and ecological sensitivity. In this review, regional ecosystems are defined as spatially bounded socio-ecological systems that integrate natural resources, infrastructure, and human communities under shared governance or land-use regimes. Within these systems, AI interventions refer to the deployment of machine learning models, digital twins, IoT-based monitoring, and related data-driven technologies designed to sense, predict, or manage environmental and socio-economic dynamics. The impact on sustainability is assessed in terms of how such interventions influence ecological resilience, resource efficiency, and social equity across the environmental, economic, and social pillars of sustainable development. These technologies encompass a wide range of applications: machine learning algorithms are employed for classification, prediction, and anomaly detection across agricultural and environmental datasets; digital twins provide real-time virtual representations of physical assets such as irrigation systems or urban infrastructure; IoT sensors enable continuous environmental monitoring and data collection, while remote sensing technologies support large-scale land-cover analysis and resource tracking. This technological landscape forms the basis for AI interventions across multiple ecosystem types and sustainability goals. Recent studies confirm the high applicability of AI in digital soil mapping [1], urban planning [2], agriculture [3], industrial process optimization [4], and energy systems [5]. Particular attention has been paid to the role of AI in mitigating climate risks [6], supporting the digital transformation of food systems [7; 8], and contributing to ecological meteorology and ecosystem services monitoring [9; 10]. There is also a growing interest in the integration of AI for carbon flow assessment, environmental risk prediction, and the adaptation of agroecosystems to climate change [11; 12; 13]. However, despite the rapid expansion of research on artificial intelligence, there remains a lack of studies that systematically integrate cross-sectoral experiences of AI application in local ecosystems while accounting for territorial characteristics and the interdisciplinary context of sustainability. This gap hinders a comprehensive understanding of how digital transformation driven by AI affects sustainability at regional and local levels, where technological implementation often encounters social, institutional, and environmental barriers. The absence of a holistic perspective limits the development of strategies for equitable digitalization and adaptive governance in the face of escalating climate, economic, and demographic challenges. This article seeks to bridge this gap by providing a scoping review of 198 peer-reviewed sources published between 2010 and March 2025. The aim is to systematize practical AI-based solutions implemented across various types of local ecosystems, including aquatic, forest, agricultural, industrial, urban, and social systems. Particular attention is devoted to the role of AI in enhancing the resilience of agricultural regions, where digital technologies not only improve production efficiency, but also influence broader socio-environmental dynamics. Innovations such as precision farming, crop yield forecasting, and water resource optimization have demonstrated the potential to reduce environmental pressure while contributing to food security [14]. Yet, the long-term ramifications of technological transformation for agri-industrial territories remain largely underexplored. This review aims to synthesize and systematize the existing body of scholarly literature to illustrate how AI and machine learning (ML) methods contribute to the sustainable development of regional ecosystems. It also seeks to identify key thematic directions, integration barriers, and success factors associated with AI deployment in agricultural regions.