This paper introduces a novel generative AI framework that combines a variational autoencoder (VAE) with a quantitative structure-activity relationship (QSAR) model to efficiently discover synthetically viable odorant molecules, addressing the challenge of limited olfactory data. The framework generates novel chemical structures with desirable olfactory properties, validated for synthetic accessibility and stability.