Deep learning models for DDoS attack detection in IoT networks is a rapidly growing research area that focuses on leveraging neural networks to identify and mitigate distributed denial-of-service (DDoS) attacks targeting heterogeneous IoT devices. Traditional intrusion detection systems struggle with high-volume, high-dimensional, and rapidly evolving IoT traffic, whereas deep learning approaches provide automated feature extraction, temporal modeling, and adaptive detection capabilities. Early methods employed multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) for traffic classification, while recurrent neural networks (RNNs) and long short-term memory (LSTM) networks captured temporal dependencies in network traffic flows. Recent advances integrate hybrid architectures, attention mechanisms, graph neural networks (GNNs) for modeling device interactions, and federated learning for distributed, privacy-preserving detection across IoT networks. Applications span smart homes, industrial IoT, healthcare, and smart cities, where real-time, accurate, and scalable DDoS detection is critical. Current research also emphasizes handling imbalanced and non-i.i.d. traffic data, reducing false positives, optimizing computational efficiency for resource-constrained devices, and enhancing robustness against adversarial attacks, establishing deep learning as a key technology for securing IoT infrastructures.