Insider threats represent one of the most challenging cybersecurity risks, as they originate from individuals with legitimate access to organizational systems and data. Traditional security mechanisms often fail to detect such threats due to their reliance on signature-based or rule-based approaches that lack contextual awareness. Behavioral analytics, powered by machine learning (ML), has emerged as a transformative approach for identifying anomalous patterns indicative of insider misuse, fraud, or sabotage. This review explores the integration of behavioral analytics and ML techniques to enhance insider threat detection capabilities. By leveraging user activity logs, network traffic data, and system interactions, ML models can establish baseline behavioral profiles and identify deviations in real time. The study examines supervised, unsupervised, and hybrid learning approaches, highlighting their effectiveness in detecting both known and unknown threats. Additionally, it discusses feature engineering, data preprocessing, and the role of contextual information in improving detection accuracy. Challenges such as data imbalance, privacy concerns, adversarial behavior, and model interpretability are also critically analyzed. The review further explores emerging trends, including deep learning, graph-based analytics, and explainable AI, which are shaping next-generation insider threat detection systems. Ultimately, behavioral analytics