Soil tillage remains a critical agricultural operation influencing soil structure, water infiltration, erosion susceptibility, and crop productivity, yet conventional uniform tillage practices fail to address the inherent spatial heterogeneity of soil properties across agricultural landscapes. Digital soil mapping (DSM) has emerged as a transformative approach for characterizing soil spatial variability and enabling site-specific tillage management decisions that optimize agronomic performance while minimizing environmental degradation and energy consumption. This article examines the integration of DSM techniques into precision tillage systems, focusing on data acquisition methods including proximal sensing, remote sensing, and legacy soil surveys, spatial modeling approaches utilizing geostatistics and machine learning algorithms, and the translation of soil property maps into actionable tillage management zones. Key soil attributes relevant to tillage decisions—including texture, compaction, moisture retention, and organic matter distribution—are assessed for their spatial prediction accuracy and operational utility. The synthesis demonstrates that DSM-guided variable tillage significantly reduces soil erosion by up to 40%, decreases fuel consumption by 15-30%, and improves crop yield stability across diverse pedological conditions. Implementation challenges persist regarding sensor calibration, model validation across heterogeneous landscapes, and technology adoption at farm scales. Future developments in autonomous machinery integration, real-time sensor fusion, and edge computing promise to enhance the precision and accessibility of DSM-based tillage systems, advancing sustainable intensification objectives in modern agriculture while preserving soil resources for future generations.