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The increasing sophistication of cyber-attacks targeting user authentication systems has rendered traditional rule- based security mechanisms inadequate for protecting digital identities. Account takeover attacks achieved through credential stuffing, phishing, and brute force techniques pose severe threats to organizations and individuals. This comprehensive review examines five foundational research areas that form the theoretical and practical basis for developing machine learning based authentication anomaly detection systems. The review analyzes unsupervised learning algorithms with emphasis on Isolation Forest for efficient outlier detection, real-time detection pipeline architectures for low-latency cyber security applications, ensemble frameworks combining multiple algorithms for improved accuracy, deep learning approaches utilizing auto encoder architectures for behavioral modeling, and visual analytics techniques supporting security operations. Our comparative analysis demonstrates that Isolation Forest achieves optimal balance between detection accuracy (92-96%) and computational efficiency (2.3ms latency), while ensemble methods reach highest performance (95-99% accuracy) with significant false positive reduction. Geographical features exhibit highest importance (0.28) in detection, followed by device attributes (0.22). Critical implementation considerations include feature engineering strategies, threshold optimization balancing security and user experience, cascading architectures for computational efficiency, continuous learning mechanisms, and interpretability requirements. The findings provide actionable guidance for practitioners and identify research gaps including cold start problems, adversarial robustness, privacy-preserving techniques, and standardized evaluation benchmarks.