The Internet of Things (IoT) is evolving rapidly in many sectors, including personal health, home automation, industrial controls, and smart city infrastructures, with the number of connected devices constantly increasing. However, this growth has also increased the vulnerability space for Distributed Denial of Service (DDoS) attacks, which are becoming more frequent and sophisticated, making it difficult to detect them using conventional methods. To address this issue, machine learning approaches, especially deep learning-based techniques, have emerged as powerful tools for detecting and mitigating DDoS attacks. This paper reviews the state-of-the-art deep learning techniques for detecting DDoS attacks in the IoT. We have analyzed the origin, evolution, and taxonomy of DDoS attacks, benchmark datasets, ongoing research challenges, and future research directions.