Making decisions amid uncertainties poses one of the central problems in such areas as financial risk analysis and innovative sustainable development of business organizations. Classical binary logical models are unable to properly reflect vagueness and imprecision typical of actual decision-making environment. This paper provides an in-depth study of mathematical fuzzy logic models used in decision making processes. In particular, we analyze the theoretical background of fuzzy set theory as well as its different modifications like intuitionistic, hesitant, and fuzzy N-bipolar soft sets and apply these models in the context of multi-criteria decision making. Our approach is based on the use of Mamdani-type fuzzy inference system in conjunction with genetic algorithm for fine-tuning of the parameters. Quantitative analysis conducted through simulation shows that the proposed fuzzy-genetic model possesses the classification accuracy of over 84%.