  This systematic review focuses on analyzing the use of machine learning
techniques for identifying and quantifying analytes in various electrochemical
applications, presenting the available applications in the literature. Machine
learning is a tool that can facilitate the analysis and enhance the
understanding of processes involving various analytes. In electrochemical
biosensors, it increases the precision of medical diagnostics, improving the
identification of biomarkers and pathogens with high reliability. It can be
effectively used for the classification of complex chemical products; in
environmental monitoring, using low-cost sensors; in portable devices and
wearable systems; among others. Currently, the analysis of some analytes is
still performed manually, requiring the expertise of a specialist in the field
and thus hindering the generalization of results. In light of the advancements
in artificial intelligence today, this work proposes to carry out a systematic
review of the literature on the applications of artificial intelligence
techniques. A set of articles has been identified that address electrochemical
problems using machine learning techniques, more specifically, supervised
learning.
