The rising prevalence of chronic diseases has led to a significant increase in the number of patients with chronic conditions requiring mechanical ventilation, making ventilator dependence an increasingly prominent clinical issue. The status of underlying chronic disease serves as a pivotal determinant in the development and progression of ventilator dependence. In patientwith chronic diseases, ventilator dependence involves complex multisystem interactions, including respiratory, cardiovascular, neuromuscular, and metabolic systems, often presenting with phenotypic overlap and dynamic evolution. The development of an artificial intelligence-driven predictive system—centered on chronic disease profiles and integrating multidimensional data such as baseline chronic disease status, real-time physiological parameters, imaging features, biomarkers, laboratory indices, and therapeutic responses—holds significant promise for reducing the incidence of ventilator dependence and promoting the advancement of critical care medicine toward precision diagnosis and treatment. This article discusses the core role of chronic disease status, phenotypic diversity of ventilator dependence, and AI applications, aiming to provide theoretical basis and practical insights for establishing a precision prediction system for ventilator dependence centered on chronic diseases.