As analytical techniques improve, we are able to measure more and more things in biofluids and tissue samples. These advancements in analytical chemistry have given us a new problem of being able to understand and interpret all this data. Big data requires different techniques that can more efficiently analyze, interpret, and visualize the data that regular techniques are not able to handle. The utilization of Artificial intelligence provides the solution which allows us to gain the knowledge from the data we are studying. With machine learning techniques, we can analyze our clinical data together with complex analytical metabolite measurements to identify the markers that carry the most significant information about the disease in question. We can also use additional techniques to learn these patterns, then recognize or predict similar patterns in new patients, which can be used to provide a highly accurate diagnostic.