The integration of Artificial Intelligence (AI) into Quality Management (QM) systems is transforming how organizations approach automated decision-making. This paper explores the convergence of AI technologies such as machine learning, natural language processing, and predictive analytics with established QM frameworks, including ISO 9001 and Six Sigma, to enhance decision support systems (DSS). We propose a conceptual model that embeds AI into key QM processes, enabling real-time data analysis, anomaly detection, and adaptive process control. Case studies from manufacturing demonstrate how AI-driven DSS can improve accuracy, efficiency, and compliance while reducing human error. The study also examines the ethical and operational implications of AI in quality-centric environments, highlighting challenges in transparency, accountability, and system integration. Our findings suggest that synergizing AI with QM not only accelerates continuous improvement but also redefines quality assurance in the era of Industry 4.0.