Nonconformance management and Corrective and Preventive Action (CAPA) systems are central components of modern laboratory quality management frameworks. This review critically examines the effectiveness of CAPA systems in research, clinical, industrial, and testing laboratories, with emphasis on laboratory quality standards, technological advancements, implementation challenges, and emerging trends. The review synthesizes evidence from international standards, regulatory guidelines, and recent peer-reviewed literature on the sources of laboratory nonconformances, including human factors, equipment failures, documentation errors, weak quality culture, and data integrity issues. Particular attention is given to root cause analysis methods, risk-based CAPA approaches, and the integration of Laboratory Information Management Systems (LIMS), artificial intelligence (AI), predictive analytics, and digital audit trails into laboratory quality systems. The review identifies persistent weaknesses affecting CAPA effectiveness, including superficial root cause investigations, inadequate personnel training, poor documentation practices, financial limitations, resistance to technological adoption, and excessive compliance-driven approaches that prioritize audit readiness over continuous improvement. Contradictions within the literature reveal that the effectiveness of digital and AI-driven CAPA systems depends heavily on implementation quality, staff competency, and organizational culture. Significant research gaps were identified, particularly the limited availability of empirical studies from developing countries, inadequate assessment metrics for CAPA effectiveness, weak integration of behavioral factors into quality models, and the absence of unified CAPA maturity frameworks for laboratories. The review concludes that future laboratory quality systems must integrate technological innovation with human-centered quality management principles to achieve sustainable improvements in compliance, operational reliability, and continuous quality enhancement.

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