Osteoarthritis (OA) is a leading cause of global disability, characterized by progressive joint degeneration. Current management remains limited to symptomatic treatment, lacking strategies to modify disease progression or provide personalized care. As an emerging multi-scale modeling framework, the artificial intelligence-driven virtual cell (AIVC) aims to integrate molecular, cellular, and tissue-level data to systematically simulate the pathogenesis of OA. By leveraging advanced architectures such as geometric deep learning, diffusion models, and graph neural networks, AIVC has the potential to resolve the complex interactions that drive OA, including inflammatory cascades and mechanobiological feedback loops. Its key application prospects include virtual drug screening, patient-specific digital twin construction, and mechanism-based precision intervention stratification. Although closed-loop validation schemes integrating AIVC technology with organoid models and robotic experiments are still in the early stages of development, and challenges such as data standardization and model interpretability persist, AIVC offers a transformative paradigm for shifting OA management from reactive treatment to proactive, personalized prevention. Realizing this potential will require close cross-disciplinary integration and collaboration among computational biology, rheumatology, artificial intelligence, and bioengineering.