Deep learning-based attack detection for the RPL (Routing Protocol for Low-Power and Lossy Networks) is a specialized research area in IoT and network security that focuses on identifying malicious activities and routing attacks in constrained IoT networks. RPL is widely used in low-power and lossy networks (LLNs) for smart grids, industrial IoT, and wireless sensor networks, but it is vulnerable to attacks such as rank attacks, wormhole attacks, sinkhole attacks, and selective forwarding. Traditional intrusion detection systems often struggle with dynamic network topologies and resource constraints, whereas deep learning approaches offer automated feature extraction, temporal pattern recognition, and adaptive threat detection. Early studies used multilayer perceptrons (MLPs) and recurrent neural networks (RNNs) to classify network traffic, while recent research integrates long short-term memory (LSTM) networks, convolutional neural networks (CNNs), autoencoders, and attention mechanisms to capture spatio-temporal dependencies in RPL network flows. Applications focus on securing smart homes, industrial IoT, and healthcare networks by detecting attacks in real-time and preventing routing disruptions. Current research also explores federated learning for distributed detection, lightweight models for resource-constrained nodes, robustness against adversarial manipulation, and hybrid architectures for improved accuracy and efficiency, establishing deep learning as a promising approach for securing RPL-based IoT networks.