In the field of geosciences research, due to the large amount of complex data, clustering methods are necessary as a first approach to provide insight into the data without previous analysis. Uniform Manifold Approximation and Projection (UMAP) has recently gained popularity as an effective clustering algorithm in several fields. We explore the application of UMAP as a pre-processing technique to enhance clustering results on 89 geochemical samples from the sediments of the Rapel River fluvial system with 25 variables. The clustering method was performed using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDSBSCAN), and the results were evaluated using the Silhouette Score. The generated clusters were then compared to the data origin results and interpretation. We demonstrate that UMAP can construct a favorable representation for clustering purposes, distinguishing different rivers denoting their own characteristics based on their geochemical data.