In medical applications, the demand for explainable AI systems has driven the adoption of symbolic methods, such as Fuzzy Inference Systems, known for their interpretable fuzzy rules and ability to facilitate communication between AI systems and human users. Recently, hybrid approaches that combine sub-symbolic (data-driven) and symbolic methods have gained significant attention due to their capacity to leverage the strengths of both paradigms. For example, Fuzzy Neural Networks integrate the predictive power of neural networks with the interpretability of fuzzy systems. At the same time, Evolutionary Algorithms further improve these models by optimizing parameters, improving performance, and increasing adaptability. This paper proposes a novel evolutionary fuzzy neural network framework incorporating a genetic algorithm to enhance classification capabilities while preserving model transparency. Our architecture integrates evolutionary optimization into the parameter update process of an existing Fuzzy Neural Network. An extensive validation on the Maternal Health Risk dataset demonstrates the framework’s effectiveness in balancing predictive accuracy and explainability. GitHub: https://github.com/IDA-FBK/NeuroFuzzyProject