The modern business climate, with its dynamic transformations and abundance of data, subjects organizations to major pressure to make sound decisions in an uncertain environment. The traditional decision-support methods are not able to properly deal with ambiguity caused by the lack of complete information, qualitative variables, and imprecise judgments. In order to address these constraints, this paper will come up with an integrated framework that would utilize cognitive computing as well as fuzzy logic models to make decisions that are more relevant to organizations in uncertain situations. An advanced type of artificial intelligence, cognitive computing is capable of human-like reasoning despite not being capable of reflecting vagueness in human judgment as a formal mechanism. On the other hand, fuzzy logic offers a mathematical basis for processing vague data, language uncertainty, and man-based knowledge modeling. The proposed study will be able to combine all these efforts to present two hybrid fuzzy-cognitive models that can be used to aid in risk management, performance evaluation, and strategic planning of an organization. The fuzzy inference systems incorporate domain knowledge and real-world variability, and cognitive algorithms result in the processing of massive heterogeneous data to produce contextualized knowledge. Simulations are performed to test the models for accuracy, adaptability to uncertainty, and performance of the decisions. Findings prove that fuzzy logic is very useful when combined with cognitive computing in improving the responsiveness, decision accuracy, and agility of an organization. The framework proposed not only enhances alignment of all involved parties but also gives organizations the power to succeed in uncertain and volatile environments. The work helps in the development of intelligent enterprise systems and indicates a good future direction for decision support in uncertain situations.