<p>Balancing predictive performance with transparency remains a core challenge in eXplainable Artificial Intelligence, especially for tabular data. In this work, we present FuzzyGNN, a novel hybrid framework that combines fuzzy logic with Graph Neural Networks (GNNs) to deliver both high accuracy and interpretability. FuzzyGNN represents each data instance as a graph, where raw features and fuzzy sets are modeled as nodes, enabling contextual reasoning through message passing. Unlike traditional neuro-fuzzy systems—often limited by scalability and rule explosion—FuzzyGNN enforces model compactness through its graph-aware architecture and a pruning mechanism guided by graphbased explanations. Extensive experiments on seven benchmark datasets show that FuzzyGNN achieves competitive predictive performance while drastically reducing the number of fuzzy rules, linguistic terms, and active features used to produce output fuzzy rules. Additionally, it offers structural interpretability through rule extraction and post-hoc explainability via GNNExplainer. These capabilities make FuzzyGNN particularly well-suited for domains where transparency is as crucial as accuracy, such as healthcare, finance, and safety-critical systems. The code is available in the following link: <a href="https://github.com/cilabuniba/fuzzygnn">https://github.com/cilabuniba/fuzzygnn</a></p>