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The discovery of functional dye materials with superior optical properties is crucial for advancing technologies in biomedical imaging, organic photovoltaics, and quantum information systems. Recent advancements highlight the need to accelerate this discovery process by integrating computational strategies with experimental methods. In this regard, we have employed a computational approach to explore the latent space of dye materials, utilizing swarm optimization techniques to efficiently navigate complex chemical spaces and identify optimal values of molecular properties using machine learning methods based on target properties, such as high extinction coefficients ($$\varepsilon$$). The latent space based evaluation outperformed all available features of a domain. This approach enhances inverse material design by systematically correlating molecular parameters with desired optical characteristics by implementing VAEs. In this process, by defining target properties as inputs, the model effectively determines the key molecular features necessary for engineering high-performance dye compounds.