Today Cyberattack are heavily automated, relying on AI based intrusion methods and fast evolving malware to evade network defense mechanisms. Static honeypots are used for threat monitoring, but they can be quickly identified by professional attackers, limiting their usefulness in practice. This work develops an AI-Powered Polymorphic Honeypot (AIPPH), which facilitates adaptive, intelligent, and stealthy threat deception for advanced network security systems. The above- mentioned approach integrates machine-learning-based behavioral analysis, dynamic environment generation, and polymorphic service emulation for the honeypot to evolve all its system signatures, network behavior, responses, and operating- system-level characteristics in real time. This flexibility greatly improves engagement times by attackers and minimizes the risk of honeypot detection. A real-time threat intelligence module deepens the capabilities of the system by clustering attacker behavior and discovering previously unknown zero-day attacks.