The rising complexity of digital infrastructures has significantly increased organizations’ vulnerability to insider threats—malicious or negligent actions originating from within the organization. Traditional security mechanisms often fail to detect such threats, as insiders typically operate with legitimate credentials, making their malicious intent difficult to distinguish from routine activity. As cybersecurity paradigms shift toward proactive threat identification, machine learning (ML) has emerged as a pivotal tool in uncovering subtle, context-dependent behavioral anomalies indicative of insider compromise. This paper explores the application of machine learning in identifying anomalous user behavior patterns to enhance forensic capabilities in insider threat detection. It begins by examining the evolving nature of insider threats, including data exfiltration, privilege misuse, and sabotage, and highlights the limitations of signature-based detection systems. The study then categorizes ML techniques into supervised, unsupervised, and hybrid approaches, analyzing their efficacy in behavior profiling, anomaly scoring, and adaptive learning under real-world enterprise constraints. Particular emphasis is placed on unsupervised learning models—such as clustering and autoencoders—which can operate without labeled threat data and dynamically model user activity baselines. The paper also presents a forensic framework that integrates ML-based behavioral analytics with system logs, access records, and contextual metadata, enabling security teams to trace the timeline, method, and intent of insider incidents with higher precision. Through case studies and experimental simulations, the proposed ML-based approach is shown to improve detection accuracy, reduce false positives, and support incident response workflows. The paper concludes by offering design recommendations for secure, ethical, and interpretable deployment of ML in insider threat forensics.