Threat hunting is a proactive methodology for exploring, detecting and mitigating cyberattacks within complex environments. As opposed to conventional detection systems, threat hunting strategies assume adversaries have infiltrated the system; as a result they proactively search out any unusual patterns or activities which might indicate intrusion attempts. Historically, this endeavour has been pursued using three investigation methodologies: (1) Hypothesis-Driven Investigations; (2) Indicator of Compromise (IOC); and (3) High-level machine learning analysis-based approaches. Therefore, this paper introduces a novel machine learning paradigm known as Threat Trekker. This proposal utilizes connectors to feed data directly into an event streaming channel for processing by the algorithm and provide feedback back into its host network. Conclusions drawn from these experiments clearly establish the efficacy of employing machine learning for classifying more subtle attacks.