Wireless Sensor Networks (WSNs) are increasingly used for real-time monitoring across various critical applications, including environmental sensing, infrastructure monitoring, and healthcare. However, WSNs face significant challenges in anomaly detection due to their resource constraints, dynamic topology, and the complexity of high-dimensional sensor data with varying patterns. These challenges make traditional methods ineffective, highlighting the need for innovative approaches. Traditional anomaly detection methods often struggle to handle complex, high-dimensional sensor data with varying patterns. To address these challenges, we propose Hybrid Dynamic Kernel Neural Learning (HDK-NL), a novel framework that integrates deep neural networks with dynamic kernel selection for efficient and accurate anomaly detection in WSNs. HDK-NL ensuring robust detection of both spatial and temporal anomalies. A dynamic kernel hierarchy is introduced, which automatically selects kernel types (Gaussian, polynomial, linear) based on statistical properties of the extracted features, improving the algorithm's capacity to discern intricate patterns. The algorithm employs a multi-scale scoring system that aggregates anomaly scores from multiple layers, considering both local and global contexts. To optimize energy consumption in WSNs, context-aware adaptive thresholding is used to minimize false positives and reduce unnecessary transmissions. The proposed method is evaluated on real-world sensor data, demonstrating improved detection accuracy, reduced false alarms, and significant energy savings. HDK-NL offers a scalable and adaptive solution for anomaly detection in WSNs, making it suitable for resource-constrained environments that require real-time processing.