The rapid evolution of next-generation networks (NGNs), driven by 5G, IoT, edge computing, and software-defined networking, has introduced new opportunities alongside complex security challenges. Traditional intrusion detection and prevention systems (IDS), built on signature-based and anomaly-based methods, struggle to cope with the scale, heterogeneity, and dynamic threat landscape of NGNs. In response, artificial intelligence (AI) has emerged as a powerful enabler of modern IDPS. This review surveys AI-powered approaches, beginning with classical machine learning methods such as decision trees, support vector machines, and random forests, and then examining deep learning architectures including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and autoencoders. It further analyses hybrid frameworks that integrate ensemble learning, federated learning, and meta-learning, as well as specialised methods tailored for SDN, IoT, edge, and cloud/5G environments. Benchmark datasets, including NSL-KDD, CICIDS2017, UNSW-NB15, Bot-IoT, IoT-23, and TON_IoT, are reviewed, highlighting their contributions and limitations. The paper identifies key challenges, including dataset scarcity, generalisation gaps, computational overhead, adversarial robustness, explainability, and privacy. Future directions emphasise the need for realistic NGN datasets, lightweight yet accurate architectures, privacy-preserving and federated frameworks, and integrated detection and prevention mechanisms. Overall, AI-powered IDPS demonstrate significant potential to secure NGNs, but realising this vision will require advances that balance accuracy, efficiency, interpretability, and resilience
