Wireless Sensor Networks (WSNs) are indispensable in modern IoT ecosystems, enabling critical applications from industrial automation to environmental monitoring. However, their reliability is frequently compromised by node failures, environmental interference, and energy constraints. Traditional fault diagnosis methods, whether rule-based or purely data-driven, struggle to balance accuracy, interpretability, and computational efficiency. This paper proposes a hybrid AI framework that integrates machine learning (ML) with fuzzy logic and expert systems to revolutionize fault prediction and diagnosis in WSNs. By leveraging ML’s pattern recognition strengths and the contextual reasoning of fuzzy-expert systems, the framework achieves 96% fault detection accuracy, 45% reduction in false positives, and 35% lower energy consumption compared to standalone approaches. Validated through synthetic simulations (NS-3) and real-world datasets (Intel Lab, AgriSense), the model enables predictive maintenance, reducing downtime by 70% in industrial use cases. This study bridges the gap between data-driven innovation and human-centric design, offering a blueprint for sustainable, resilient WSNs.