This paper explores the application of fuzzy neural networks in improving service quality. Fuzzy neural networks combine the power of neural networks with the flexibility of fuzzy logic, enabling more accurate and efficient decision-making processes. Existing research on fuzzy neural networks includes applications in image labeling, clustering, image recognition, adaptive neural networks, fixed-time synchronization, and delay-independent nonlinear fuzzy control. Novel architectures like self- organizing direction-aware data networks and multi-functional recurrent fuzzy neural networks are also proposed. The application of fuzzy neural networks holds great potential for improving service quality. By combining the power of neural networks with the flexibility of fuzzy logic, these networks can provide more accurate and efficient decision-making processes by synergizing machine learning with fuzzy reasoning, the proposed model effectively handles the uncertainty and imprecision characteristic of network environments, enabling precise classification of service quality levels and early detection of anomalous patterns. Experimental validation on real-world telemetry datasets demonstrates that the data-driven methodology significantly outperforms static rule-based systems, achieving superior accuracy in quality of service categorization while drastically reducing false positive rates. Furthermore, the interpretable nature of the generated fuzzy rules provides network operators with actionable insights, bridging the gap between black-box artificial intelligence and operational explainability. The findings confirm that integrating data-centric learning with fuzzy logic offers a resilient, scalable, and highly accurate solution for proactive service assurance and autonomous network management (pp.71-80).