Exploration geochemistry is one of the most important ways to understand natural environments. The different applications of geochemical methods on the global, regional, and local scales for the exploration of mineral resources are changing. One of the applications of geochemical operations is the investigation of subsurface mineralization. Exploratory geochemistry in the form of sampling from different environments such as stream sediments, rock, soil, water, etc., and by measuring the physical properties of the earth tries to identify the possible subsurface anomalies of the earth. Also, geochemical analyses are performed with different goals. One of these goals is in the form of providing promising areas for subsequent supplementary studies. In a geochemical study, usually, a large number of variables are investigated at the same time because they may be a set of elements that model a geochemical state and not just one element. It is the interaction between different elements that shows a more appropriate picture of the area under study. Sometimes, the interaction effect only shows itself in multivariate analysis. One of the applications of multivariate analysis is to reduce the number of variables and the size of data that are presented in two or more dimensions. On the other hand, the optimization of multivariate geochemical data analysis has been increasingly popular in recent years. Performing these optimizations is often technically difficult due to the highly correlated nature and abundance of variables. However, there are many techniques for recognizing the main important variables and optimizing the geochemical analysis, which can optimize the costs, time, and percentage of success of an exploratory project. Therefore, the current chapter aims to discuss some of the most used machine learning techniques for the optimization of multivariate geochemical data analysis, which will be discussed by describing two practical examples of the two exploratory geochemical projects.