<p>Efficient multi-objective collaborative design is a core scientific challenge and a primary obstacle to the development of novel glass materials. Here, we propose IGlasGAN (Inorganic Glass Generative Adversarial Network), a generative artificial intelligence framework for the automatic inverse design of inorganic glass materials subject to box constraints on multiple coupled properties, alongside a full generation-evaluation-validation pipeline. Built upon an improved WGAN-GP-CP architecture, the framework incorporates a property navigation mechanism and an extended normalization strategy to enable end-to-end, on-demand design, from property requirements to glass compositions, across a broad design space. By training on a dataset comprising 2445 experimental samples, in which 156 samples fully satisfy the four property requirements, including coefficient of thermal expansion (CTE), Young’s modulus (E), strain point temperature (T_st), and density (ρ), a multi-objective inverse design for the OLED substrate glass utilizing IGlasGAN was carried out, and novel compositions with relatively high Y₂O₃ content was identified. Two additional design-from-scratch scenarios with progressively more distant target regions from the training data distribution were constructed to further validate the framework’s generation capability. Across the three scenarios, 12 generated samples were experimentally characterized. The measured properties agreed well with the generated values, with deviations within ±3.50%, except for CTE, which showed a maximum deviation of 13.03%. These results confirm the applicability of the IGlasGAN framework across diverse target settings, providing an efficient intelligent pathway for multi-objective inverse design of inorganic oxide glasses.</p>