Unsupervised deep learning-based anomaly detection in IoT environments is a significant research area focused on identifying abnormal behaviors, intrusions, or faults in heterogeneous and large-scale IoT networks without relying on labeled datasets. Research papers in this domain explore models such as autoencoders, variational autoencoders (VAEs), generative adversarial networks (GANs), and deep clustering techniques to detect deviations from normal patterns in sensor data, network traffic, or device behavior. Key contributions include edge/fog-assisted anomaly detection for low-latency responses, energy-efficient algorithms suitable for resource-constrained IoT devices, and hybrid frameworks combining unsupervised deep learning with traditional statistical or rule-based methods. Recent studies also address challenges such as dynamic network topologies, high-dimensional data, scalability, and adversarial attacks on deep learning models. By leveraging unsupervised deep learning, research in this area aims to provide intelligent, adaptive, and robust anomaly detection solutions, enhancing the security, reliability, and resilience of IoT ecosystems