In recent decades, the demand for higher quality and precision in manufacturing and construction has increased significantly due to globalization, customer expectations, and the complexity of modern projects. Traditional quality control (QC) systems, reliant on manual inspections and human oversight, often fall short in delivering consistency, speed, and accuracy. This review explores how artificial intelligence (AI) technologies are reshaping the landscape of quality control in both manufacturing and construction industries. The methodology employed in this review includes a synthesis of peer-reviewed journal articles, industry reports, and case studies published before 2018, focusing on practical applications of AI, such as machine learning (ML), computer vision, and robotics in quality assurance. Evidence from these sources was critically analyzed to extract common trends, successes, and challenges in implementing AI-driven QC solutions. The results indicate that AI systems outperform traditional QC processes in defect detection, predictive maintenance, and process optimization. In manufacturing, AI tools identify product anomalies in real-time with remarkable accuracy. In construction, drones, sensors, and ML algorithms ensure structural integrity, monitor progress, and minimize material waste. AI also facilitates adaptive learning, enabling systems to evolve and improve with continuous data input.The review concludes that AI-driven quality control enhances efficiency, reduces human error, and lowers operational costs in both sectors. However, successful integration demands robust infrastructure, skilled personnel, and regulatory frameworks to address ethical concerns and safety standards. Ultimately, AI's role in QC is not a replacement of human expertise but a complement that augments capabilities and fosters innovation.
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