This study introduces the GWO-FNN model, an improvement of the fuzzy neural network (FNN) architecture that aims to balance high performance with improved interpretability in artificial intelligence (AI) systems. The model leverages the Grey Wolf Optimizer (GWO) to fine-tune the consequents of fuzzy rules and uses mutual information (MI) to initialize the weights of the input layer, resulting in greater classification accuracy and model transparency. A distinctive aspect of GWO-FNN is its capacity to transform logical neurons in the hidden layer into comprehensible fuzzy rules, thereby elucidating the reasoning behind its outputs. The model’s performance and interpretability were rigorously evaluated through statistical methods, interpretability benchmarks, and real-world dataset testing. These evaluations demonstrate the model’s strong capability to extract and clearly express intricate patterns within the data. By combining advanced fuzzy rule mechanisms with a comprehensive interpretability framework, GWO-FNN contributes a meaningful advancement to interpretable AI approaches.