Wireless Sensor Networks (WSNs) have emerged as a fundamental technology in modern digital ecosystems, enabling real-time data acquisition and communication. Their integration with the metaverse enhances immersive experiences by providing real-time environmental data, motion tracking, and networked interactions. However, the fusion of WSNs with the metaverse introduces significant security challenges, including network vulnerabilities, data privacy concerns, latency issues, and scalability constraints, which hinder seamless operation. To address these challenges, Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques have been leveraged to enhance network security, optimize resource management, and improve data processing efficiency. AI-driven models facilitate anomaly detection, predictive maintenance, and real-time decision-making, making WSN-based metaverse environments more resilient and adaptive. A hybrid DL and ML model is proposed, integrating Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, followed by XGBoost for classification. The model achieves 99.64% accuracy, 96.39% balanced accuracy, 99.65% precision, 99.64% recall, and 99.73% ROC-AUC score, outperforming existing approaches. The results demonstrate its effectiveness in detecting security threats within WSN-based metaverse environments while ensuring computational efficiency and real-time attack detection.