## 3 Results  
The analysis of 155 peer-reviewed publications, covering the period from 2010 to March 2025, enabled the identification of nine key research areas in which artificial intelligence technologies are being applied to enhance the sustainability of local ecosystems. Below is a brief overview of these thematic areas and their core focus points. The first area centers on environmental monitoring and the mapping of land use, vegetation, and soil using AI and remote sensing data. These tools support high-resolution assessments of degradation processes and inform decision making in sustainable land management. Special emphasis is placed on digital soil mapping and modeling the spatiotemporal dynamics of ecosystems under the influence of urbanization and climate change. The second area focuses on the application of AI for sustainable agriculture and the management of agroecosystems. Core themes include yield prediction and land suitability assessments, monitoring of soil and vegetation health, and optimization of water and energy use. Figure 4: PRISMA flowchart. Source: compiled by authors. The third thematic area focuses on the application of AI for monitoring, forecasting, and the sustainable management of aquatic and coastal ecosystems. Key topics include the analysis of water quality and hydrological processes using machine learning, mapping of degradation and pollution risks, and forecasting water balances under climate change scenarios. AI tools improve the accuracy of environmental assessments, support ecosystem restoration efforts, and enable the development of local strategies in response to increasing anthropogenic pressures. The fourth area pertains to the use of AI in the management and conservation of forest ecosystems. It emphasizes forest fire prediction and spatiotemporal modeling, biomass and carbon stock assessment for climate strategies, 3D modeling of forest structures, habitat classification for biodiversity monitoring, and tracking of forest degradation and regeneration. The fifth area addresses carbon monitoring, vulnerability assessment, and ecosystem change forecasting using AI. Core themes include the integration of satellite data and AI to estimate greenhouse gas emissions and carbon balances, the prediction of climate risks and droughts, and the modeling of ecosystem dynamics under various environmental stressors. The sixth area is concerned with the evaluation and management of ecosystem services through AI applications. Key aspects involve the use of machine learning and remote sensing for spatiotemporal modeling and land zoning, interpretable AI models to quantify ecosystem service losses, and the incorporation of AI into strategies for sustainable tourism, landscape restoration, and natural capital valuation. The seventh thematic area explores the application of AI in regional contexts--specifically in business, industry, and agriculture. Key focal points include the transformation of small- and medium-sized enterprises (SMEs) and agricultural systems through AI, digital twins, and cloud platforms; the use of AI in precision agriculture, food system planning, and the assessment of the environmental impacts of agricultural practices; the integration of AI into industrial processes and sustainable manufacturing; and the deployment of AI for managing urban ecosystems, infrastructure, and waste. The eighth area is dedicated to the application of AI in transforming local energy systems. It highlights the use of IoT and AI to improve the energy efficiency of buildings and urban infrastructure; the optimization of distributed and renewable energy management; the development of predictive models for energy consumption and generation; and the use of AI to assess climate impacts and support the digitalization of the energy sector at the regional level. The ninth area concerns the use of AI to support local sustainable development in the domains of education, healthcare, and social inclusion.