The rapid expansion of Internet of Things (IoT) devices has brought significant cybersecurity challenges, especially in the realm of anomaly detection, which is crucial to defending against a diverse and evolving range of attacks. Conventional methods struggle to adapt to the dynamic and high-dimensional nature of IoT traffic, underscoring the need for advanced, data-driven solutions. This study presents an anomaly detection framework utilizing Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) to detect various types of attacks in IoT environments. Leveraging adversarial training, our method generates realistic data distributions, enabling the model to effectively distinguish between normal and anomalous traffic patterns. We trained and evaluated the WGAN-GP model on a comprehensive IoT dataset containing diverse attack types, including Denial of Service (DoS/DDoS), information gathering, man-in-the-middle, injection, and malware. A detailed correlation analysis helped identify feature dependencies, informing feature selection and dimensionality reduction. Experimental results demonstrate the model’s high accuracy (98.7%) and robust classification performance, with F1-scores consistently above 0.97 across attack categories. Additionally, the anomaly score distribution supports the model’s ability to detect anomalies with minimal false positives, showcasing its potential for dynamic thresholding. Our findings suggest that WGAN-GP is a promising approach for IoT anomaly detection, offering flexibility and scalability suitable for complex network environments. Future work could explore dynamic thresholding and further feature optimization to enhance detection accuracy and efficiency. This research introduces a scalable, adaptable framework for real-time IoT anomaly detection, with significant implications for improving the security and resilience of connected devices across diverse applications.