 This systematic review has identified several key findings related to ML slash DL-based DDoS attack detection in SDN networks. Firstly, it was found that ML slash DL approaches can be used to detect various types of DDoS attacks, including SYN flood, UDP flood, ICMP flood, HTTP flood, and DNS amplification attacks. Secondly, the existing literature has shown that ML slash DL approaches have been successfully applied to detect DDoS attacks with varying degrees of accuracy. Thirdly, the existing literature has also demonstrated that ML slash DL approaches have been able to achieve high accuracy rates when compared to traditional methods. Fourthly, the existing literature has provided benchmark datasets and classes of attacks for evaluating the performance of ML slash DL approaches. Fifthly, the existing literature has also discussed the pre-processing strategies, hyperparameters, experimental setups, and performance metrics used in the existing literature. Finally, the existing literature has highlighted some of the current research gaps and promising future directions. This article was authored by Tariq Ahmad Ali, Yang Wei Chong, and Selvakumar Manicum.