As cyber threats become complex and highly sophisticated, traditional security mechanisms often have a hard time detecting and thwarting novel attacks. This paper surveys the integration of honeypots, machine learning (Isolation Forest), and threat intelligence towards implementing a multi-layered approach to cybersecurity. Honeypots are deception-based mechanisms that attract and log attackers’ activities, providing tremendous insight into the cyber threat landscape. Isolation Forest is a widely used anomaly detection algorithm applied to real-time threat recognition by capturing changes to data patterns with extremely high computational efficiency. Along with considerably assisting in the identification and mitigation of cyber threats, threat intelligence uses knowledge-sharing frameworks and predictive analytics. The survey provides an insight into the way research exists on the three technologies and their efficacy in mitigating cyber threats, as well as the challenges of their implementation.