This paper provides a detailed evaluation of the integration of honeypots with cybersecurity frameworks, in-cluding Intrusion Detection and Prevention Systems (IDPS), firewalls, Security Information and Event Management (SIEM) systems, and Security Orchestration, Automation, and Response (SOAR) platforms. Using a systematic research methodology that encompasses literature review and case-based analysis, the study develops a framework for enhancing honeypot functionalities through artificial intelligence (AI) and machine learning (ML). This innovative approach enables the generation of dynamic threat intelligence, predictive analytics, and adaptive responses to advanced cyber threats. The findings underscore the role of honeypots in augmenting threat detection accuracy, reducing re-source overhead, and providing actionable insights into attacker tactics, techniques, and procedures (TTPs).