Insider threats remain one of the most complex and costly cybersecurity challenges faced by modern organizations, as malicious or negligent actions originate from trusted users who possess legitimate access to critical systems and sensitive information. Traditional rule-based detection mechanisms often fail to identify subtle behavioral deviations that precede insider incidents, resulting in delayed response and elevated organizational risk. This study proposes a behavioral analytics framework powered by machine learning techniques to detect insider threats through dynamic modeling of user activity patterns. By leveraging multi-source organizational logs, including authentication records, file access events, communication metadata, and network activity traces, the framework constructs individualized behavioral baselines and identifies anomalous deviations indicative of potential threat activity. Both supervised and unsupervised learning models are evaluated using a benchmark insider threat dataset, with careful attention to data imbalance mitigation and model interpretability. Experimental results demonstrate that ensemble learning methods and temporal modeling approaches significantly enhance detection accuracy while maintaining acceptable false positive rates. The findings underscore the importance of integrating behavioral machine learning models into Security Operations Centers to enable proactive, scalable, and context-aware insider threat mitigation strategies.