The rapid increase in the use of Internet of Things (IoT) devices has transformed everyday life and industries such as healthcare, transportation, and smart homes. However, these devices, often limited in resources, depend on communication across edge, fog, and cloud layers, creating vulnerabilities that attackers can exploit. This paper provides a comprehensive review of intelligent intrusion detection system (IDS) tailored for IoT security, focusing on solutions that utilize Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). We analyze existing IDS approaches for IoT devices and secure communication across the edge, fog, and cloud layers, highlighting their strengths and limitations. Additionally, we identify Key research challenges include computational complexity, real-time adaptability, and energy efficiency in Edge Computing. To address these gaps, we propose future research directions, including neuromorphic computing for ultra-fast IDS, self-evolving AI-driven IDS, hyper-personalized anomaly detection, federated learning for privacy- preserving security, and explainable AI (XAI) for human–AI collaboration. By integrating these innovations, we envision next-generation IDS solutions that offer scalable, interpretable, and energy- efficient security frameworks for the dynamic IoT ecosystem.