Fuzzy logic provides a powerful framework for uncertainty analysis, particularly in the association of adverse drug reactions (ADRs). This study investigates the real-world implications of leveraging a fuzzy logic-based framework compared to traditional machine learning models, such as logistic regression, decision trees, and random forests, for classifying ADR relationships. The methodology includes the systematic construction of membership functions, fuzzy rule definitions for decision-making, and application to graph data stored in Neo4j for efficient representation and exploration of ADR relationships. The results demonstrate that the fuzzy logic model achieves superior classification accuracy (1.00) both before and after cross-validation, competitive interpretability scores, and robust confidence interval coverage (76.52% after cross-validation). Additionally, visualizations of decision boundaries highlight the flexibility of fuzzy models in capturing non-linear separations. While the approach shows promise, limitations such as dependency on high-quality input data and computational complexity for large-scale graphs are acknowledged. This work underscores the potential of fuzzy logic systems for graph-based applications in scenarios where data availability is limited, decision transparency is critical, and uncertainty analysis is paramount, thereby paving the way for improved ADR management and patient safety. This work is used in healthcare systems, drug safety monitoring, or pharmaceutical development.