As industries evolve toward digital transformation, the integration of traditional quality management tools with emerging technologies becomes imperative. This paper presents a novel framework that synergizes Six Sigma methodologies with Artificial Intelligence (AI) to align with the principles of Quality 4.0. By leveraging machine learning, real-time analytics, and big data, the proposed model enhances the DMAIC (Define, Measure, Analyze, Improve, Control) cycle for continuous improvement in smart manufacturing and service environments. The framework introduces AI-powered tools for root cause analysis, predictive quality, and intelligent decision-making, thereby reducing process variation and enhancing operational efficiency. Case studies and simulations demonstrate the effectiveness of this integrated approach in driving superior quality outcomes and enabling agile responses to dynamic market demands. This research bridges the gap between statistical process control and AI-driven quality assurance, offering a scalable pathway for organizations to achieve excellence in the Industry 4.0 era.