Incorporating Artificial Intelligence (AI) into the Kano model presents significant possibilities for improving quality management, increasing customer satisfaction evaluations, and refining product or service development plans. This systematic literature review explores the current research on the integration of AI with the Kano model, highlighting significant trends, advantages, and obstacles. The data analytics capabilities of AI allow for the automation and enhancement of Kano's feature classification, hence enabling more accurate identification of customer preferences and dynamic feedback mechanisms. This study classifies the reviewed publications into two groups: those leveraging textual data (e.g., customer evaluations) and those adopting alternative analytical tools to augment the Kano model. Research demonstrates that AI-driven methodologies enhance the efficiency and precision of Kano’s framework, providing real-time insights and predictive functionalities. However, problems including quality of data, questionnaire biases, and integration issues are acknowledged, underscoring the necessity for additional study to mitigate these limitations and fully realize the potential of this integrated method.