Multi-omics analysis of cerebrospinal fluid (CSF) offers a unique window into the pathophysiology of the central nervous system. Yet, the high dimensionality of such data combined with small cohort sizes create a structural imbalance. In this setting, variables far outnumber observations, undermining the performance of supervised classification models. This challenge is especially acute in neurological diseases, where phenotypic heterogeneity demands both large and representative training sets. This review positions generative AI as a foundational response to these compounded limitations. We analyze how generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models can augment training datasets, enable and refine supervised classification. By generating synthetic samples and capturing latent disease structure, these approaches improve how accurately classifiers generalize in diagnosis and disease stratification. Generative models support trajectory modeling and the identification of biomarkers through multimodal integration. Initial studies have used GANs to generate synthetic CSF multi-omics samples, directly addressing the scarcity of patient cohorts required to train robust classifiers. VAE-based normative modeling has further demonstrated that generative approaches can capture the biological heterogeneity of CSF profiles without requiring large labeled datasets. Critical challenges related to clinical validation, interpretability, algorithmic bias, and regulatory frameworks are addressed. Generative AI would emerge as a complementary framework for supervised classification to analyze small-sample clinical settings.