<p>Accurate soil sampling is fundamental for producing reliable data in soil science, particularly when addressing spatial variability and logistical constraints. Well-designed sampling strategies that balance sample size, spatial heterogeneity, and operational costs enable effective selection of sampling locations, thereby improving parameter estimation and spatial interpolation accuracy. Despite this, real-world challenges—including limited accessibility, dense vegetation, rugged terrain, and restrictions in time and budget—often hinder the implementation of optimal sampling plans. A thorough review of published studies reveals that stratified random sampling consistently offers the highest statistical accuracy and reliability, whereas spatial coverage-oriented methods provide superior geographic representativeness. Simple random, grid-based, and spatial coverage approaches remain the most straightforward to implement, while conditioned Latin hypercube sampling (cLHS) stands out as a robust, widely recommended advanced method. Integrating stratified random and spatial coverage-based techniques emerges as the most effective strategy, maximizing statistical soundness, spatial representativeness, and cost-efficiency in soil sampling design. This review synthesizes the advantages and limitations of major statistical and geometric sampling methods, offering practical guidance for planning soil sampling campaigns that support rigorous scientific analysis and geospatial applications.</p>