The research examines in detail the computational intelligence approaches used in decision systems by studying fuzzy logic systems, artificial neural networks and support vector machines. Difficult issues with uncertainty, imprecision and complex behaviours are solved better by using these approaches than traditional techniques. The study analyzes the framework and structure of fuzzy logic systems, including the methods used for fuzzy controllers and inference, plus Adaptive Neural Fuzzy Inference Systems (ANFIS). Further, the discussion explains the way neural networks are built and what techniques they use to produce good solutions. Both PCA with Fuzzy C-Means are important initial processing steps in the investigation. This document discusses Support Vector Machines by outlining their basic functions plus the method of finding the best hyperplane and the way kernel functions help form non-linear patterns. These techniques greatly benefit decisions that should learn and adjust over time. Later research will aim to develop useful combination models and address the problems of handling massive amounts of data in different application fields.