<p>This study presents an AI-enhanced Quality Function Deployment (QFD) framework designed to overcome the limitations of traditional QFD methods, which rely on manual data collection and subjective analysis. By integrating Natural Language Processing (NLP), sentiment analysis, and Machine Learning (ML), the proposed model automates the extraction and analysis of customer feedback, enabling real-time updates to the QFD matrix and improving the alignment between customer expectations and technical requirements. A mixed-method research design was adopted, including an experimental implementation of the AI-QFD system and structured participant evaluations. Quantitative methods such as the Wilcoxon signed-rank test, correlation analysis, and multiple regression were used to assess improvements in efficiency, accuracy, and adaptability. Results indicate that the AI-powered model significantly reduces manual workload, enhances responsiveness, and strengthens the predictive capabilities of QFD. In addition, a strong positive correlation was found between trust in AI tools and users’ willingness to adopt the system. This research demonstrates the potential of AI to transform quality management practices by enabling automated, data-driven decision-making and continuous customer insight integration. The proposed framework offers practical value for organizations seeking to streamline product and service development processes and gain a competitive edge in dynamic markets. Future studies should explore large-scale implementation and address ethical and scalability challenges associated with AI-driven QFD systems.</p>