Quality indicators (QIs) are essential tools for evaluating laboratory performance across the preanalytical, analytical, and postanalytical phases of the total testing process. Recent accreditation standards, including ISO 15189:2022 and NABL 112A, emphasize risk-based thinking, performance evaluation, and continuous improvement through systematic monitoring of quality indicators. Despite their widespread adoption, quality management in many laboratories remains largely reactive, relying on the retrospective review of performance data and corrective actions after deviations have occurred. Such approaches may fail to identify emerging risks in complex and data-intensive laboratory environments. Artificial intelligence (AI) has emerged as a promising technology capable of transforming quality indicator monitoring through continuous data analysis, pattern recognition, anomaly detection, and predictive analysis. By leveraging data generated from laboratory information systems, automated analyzers, quality control programs, and operational workflows, AI can identify hidden trends and forecast quality failures before they affect the patient care. Potential applications include the prediction of specimen rejection, hemolysis, quality control instability, instrument downtime, turnaround time delays, and communication errors. This review examines the role of AI in laboratory quality management and discusses its potential to shift quality monitoring from a reactive to predictive paradigm. A novel Continuous Quality Intelligence Framework (CQIF) is proposed to illustrate how quality indicators, integrated data systems, predictive analytics, and continuous improvement processes can be combined to support proactive risk management. This framework aligns with the principles of ISO 15189:2022 and NABL 112A and provides a conceptual roadmap for future AI-enabled quality systems. The adoption of predictive quality management approaches has the potential to improve patient safety, operational efficiency, accreditation readiness, and overall laboratory performance.