Quality problem solving (QPS) is a key factor in industrial performance. However, despite the widespread use of traditional QPS approaches, they remain insufficient in addressing the evolving requirements of the manufacturing industry. To provide a structured review of the limitations of these approaches, this study conducts a scoping literature review on QPS within the manufacturing sector, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search of relevant databases covering the period 2018–2025 initially retrieved 295 papers, of which 16 were selected after applying inclusion and exclusion criteria. The analysis highlights recurring limitations of traditional QPS approaches, including insufficient knowledge management, cognitive biases and subjectivity, slowness and operational inefficiency, difficulties in managing multiple problems simultaneously, and weaknesses in causal analysis. In light of these findings, this study proposes an AI-driven framework to overcome these limitations, providing both theoretical and practical value for advancing quality management in the manufacturing industry.