The detergent industry faces serious supply chain resilience challenges due to demand volatility, supply disruptions, and unstable environmental conditions, while the application of artificial intelligence (AI) in risk management and resilience enhancement of these supply chains has been only sparsely and non‑systematically examined. This study aims to identify and explain the AI‑based components of supply chain resilience in the detergent industry. A multi‑grounded theory approach was employed, whereby data were analyzed through a qualitative meta‑synthesis of 37 selected scholarly sources and semi‑structured interviews with experts. The data were coded in three stages—open, axial, and selective—using MAXQDA 2020. The findings led to the development of an integrated model comprising five main components: agility and recovery speed, operational flexibility, visibility and information transparency, management of resilience‑related costs, and the supply chain’s risk‑taking level. In this model, AI—by enabling accurate forecasting, intelligent monitoring, optimization of supply chain flows, and data‑driven decision‑making—simultaneously strengthens the resilience and sustainability of detergent supply chains and provides a basis for designing managerial and policy interventions in the face of future uncertainties and disruptions.