Non-metric Multidimensional Scaling (NMDS) is an ordination technique used to visualize complex, high-dimensional data by representing it in a lower-dimensional space while preserving the rank order of dissimilarities between samples. Unlike Principal Component Analysis (PCA), which assumes linear relationships and normality in the data, NMDS is non-parametric and handles non-linear relationships and non-normal distributions. The main assumptions for NMDS include using accurate dissimilarity or distance measures and ensuring that data is ordinal, as NMDS does not rely on specific distributional assumptions. NMDS effectively manages high-dimensional soil data by reducing it to a comprehensible form, making it easier to interpret complex interactions between soil properties, microbial communities, and environmental factors. Its utility in soil science stems from its ability to reveal patterns and relationships that are not easily captured by other methods, thereby informing better soil management and conservation strategies. Before applying NMDS, researchers should verify the appropriateness of their dissimilarity measures, ensure sufficient sample size to represent data variability, and check that their data is suitable for NMDS to obtain reliable and meaningful results.