Abstract: The newly identified cryptic explosive breccia-type fluorite deposits in the western Guizhou fluorite ore concentration area possess significant prospecting potential. However, the brecciated textures, hydrothermal alteration, and other characteristics of this type of fluorite deposit are easily confused with those of other hydrothermal breccia-type deposits or intensely structurally altered vein-type deposits. Therefore, accurately distinguishing between cryptic explosive breccia-type fluorite deposits and basin brine-related hydrothermal filling-type fluorite deposits in the study area is one of the key scientific challenges for achieving breakthroughs in fluorite prospecting in Guizhou Province. This paper conducts a comparative study of Support Vector Machine (SVM) and Random Forest machine learning classification models using systematically collected rare earth element (REE) data from three genetic types of fluorite deposits: cryptic explosive breccia-type, magmatic hydrothermal-related filling-type, and basin brine-related hydrothermal filling-type. This is combined with comprehensive analysis, including statistical analysis based on Principal Component Analysis (PCA), dimensionality reduction visualization, and quantitative evaluation using an REE separation scoring system.The results indicate that the discriminant model constructed by SVM exhibits significantly higher accuracy and stability compared to Random Forest, enabling more effective discrimination among these three genetic types of fluorite deposits. Furthermore, it identifies a refined candidate pool of key elements that can be used to distinguish them. Newly constructed discriminant diagrams—Tb/Dy vs Sm/Yb,δCe vs Sm/Yb,δCe vs Sm/Tm,δEu vs Sm/Lu—have been developed, which effectively differentiate among cryptic explosive breccia-type, magmatic hydrothermal-related hydrothermal filling-type, and basin brine-related hydrothermal filling-type fluorite deposits.