Technological innovation—driven by satellite remote sensing, geospatial analytics, machine learning (ML), and Internet-of-Things (IoT) sensor networks—is transforming soil science from descriptive mapping to predictive, near-real-time decision support. Digital Soil Mapping (DSM) integrates the SCORPAN framework with dense covariate stacks (terrain derivatives, spectral indices, climate layers) and modern ML/ensemble models (Random Forest, Gradient Boosting, Convolutional Neural Networks) to produce high-resolution, three-dimensional soil property maps (e.g., Soil Grids). Remote-sensing platforms such as Sentinel and Landsat provide critical spectral and thermal inputs that—when combined with in-situ and hyperspectral laboratory spectroscopy—enable rapid estimation of soil organic carbon (SOC), texture, moisture, pH, and other indicators. Edge AI, autonomous field platforms, and IoT sensor meshes allow continuous soil health monitoring and enable precision interventions (variable-rate fertilization, irrigation scheduling) that improve nutrient-use efficiency (NUE) and reduce environmental losses. Explainable AI (XAI) methods (SHAP, LIME, partial dependence plots) are increasingly critical to translate model outputs into actionable guidance for land managers. Key challenges remain: model transferability across soil-forming contexts, standardization and interoperability of soil observatories (FAIR data), bias from surface cover in remote sensing-derived predictions, and the need for robust validation frameworks. This chapter synthesizes theory, methods, case studies, limitations, and research directions for the DSM→AI→Precision Agriculture pipeline. This chapter presents a comprehensive synthesis of Digital Soil Mapping (DSM), Artificial Intelligence (AI), and Precision Agriculture technologies transforming soil science. The integration of remote sensing platforms, machine learning algorithms, IoT sensor networks, and decision support systems enables predictive soil intelligence at unprecedented spatial and temporal scales. Advances in ensemble modeling, deep learning for spectral soil analysis, uncertainty quantification, and autonomous soil monitoring are discussed. The chapter further examines challenges related to model transferability, sensor calibration, interpretability, and data governance. Emerging research directions including physics-informed machine learning, federated learning, and agroecosystem digital twins are explored.