As smart cities increasingly more combine the Internet of Everything (IoE), balancing safety and connectivity becomes paramount. This chapter explores the “AIoE Paradox”—the venture of making sure sturdy cybersecurity whilst retaining seamless connectivity in high-quality clever towns. We advise a singular framework for AI-driven cyber danger detection encompassing records series from diverse IoE gadgets, feature mining, anomaly detection, danger classification, and reaction mechanisms. The framework employs advanced gadget learning techniques to become aware of and cope with cyber threats in real-time, leveraging facts from sensors, cameras, network logs, and smart meters. Key findings screen the need for scalable, real-time solutions capable of handling extensive data volumes and adapting to evolving threats. Our studies highlight gaps in modern techniques, particularly in integrating decentralized protection fashions and addressing ethical worries. The look at emphasizes the importance of developing adaptive, transparent AI systems and suggests future research directions, inclusive of the combination of rising technology and the refinement of privacy-preserving techniques. This work contributes to advancing clever town security at the same time as optimizing connectivity.