Artificial Intelligence in Soil Management: Implications for Climate Resilience—A Comprehensive Review

Climate change is accelerating soil degradation, threatening agricultural sustainability and soil climate resilience. Key climate stressors include drought, altered precipitation, extreme rainfall, erosion and salinization. Artificial Intelligence (AI), combined with advanced sensing technologies, offers new opportunities for data-driven soil management under dynamic environmental conditions. This review aims to critically examine recent advances in AI-based approaches for soil monitoring, modelling and decision support, with an emphasis on soil management practices that can contribute to enhancing soil climate resilience. We analyze machine learning and deep learning methods applied to multisource data, from field observations, laboratory analyses, proximal sensing, remote sensing, meteorological records, geospatial information and agricultural management data, to predict key soil properties, including moisture, nutrient status, carbon dynamics and indicators of degradation. A total of 106 peer-reviewed studies were analysed. This review further explores AI-driven strategies for adaptive soil management, including precision irrigation, fertilization management and early-warning systems for drought, salinity and soil degradation. Key challenges, such as data heterogeneity, model explainability, sensor reliability and transferability across soil types and climates, are also discussed. Finally, emerging directions, including explainable AI, multimodal data fusion and digital soil twins, are highlighted, positioning AI-enabled sensing as a basis for climate-resilient soil management.