Typically, machine learning and optimization of complex problems are treated as distinct domains. This paper introduces two approaches on integration of these two fields, utilizing the neural network tool in function optimization problems. The first approach provides for function approximation in optimization problems, while the second provides a tool based on neural network architecture for localization of the extremum points. The conducted tests affirm the accuracy and effectiveness of the proposed approaches for the test cases with small sample datasets.