Journal of Agronomy and Field Innovations

Climate change alters the circumstances under which crops are produced, increasing the need for agroecosystem management that accomplishes several goals: high productivity, efficient use of precious resources, and the ability to adapt to changing and extreme climate conditions. In this paper, we summarize the findings of research articles on the combination of AI (artificia l intelligence), RS (remote sensing), and GIS (geographic information systems) technology in the planning of climate-friendly agroecosystems. We aim to illustrate the contribution of these technology fields to four main functions of agroecosystems: crop and environmental monitoring, climate impact and yield forecasts, assessment of the suitability of lands and resources, and carbon and biodiversity assessments. Artificial intelligence approaches such as ML (machine learning) and DL (deep Learning) are highly efficient and capable of forecasting crop yields and drought consequences based on agricultural, meteorological, and climate data. AI technologies are constantly developing and achieving very high results concerning crop yield forecasting. Suitability maps, and vulnerability assessments that support field- and landscape-scale decision-making. The literature further shows growing application of these integrated technologies to soil organic carbon monitoring, carbon stock assessment, and biodiversity-relevant land-cover characterization, extending their relevance beyond productivity toward broader ecosystem-service outcomes. Despite this progress, we identify persistent constraints, including fragmented data interoperability, limited model transferability across agroecological zones, uneven ground-truth validation, and barriers to adoption among smallholder and resource-constrained farming systems. We conclude that AI–RS–GIS integration constitutes a scientifically active and increasingly synergistic foundation for climate-resilient agroecosystem design, and we outline research priorities needed to move from demonstrated technical performance toward equitable, operational deployment.