This research introduces a framework for applying few-shot learning techniques to rare disease genomics, where data is inherently scarce, fragmented, and privacy-sensitive. The approach combines self-supervised genomic and proteomic models for transferable feature representations, meta-learning classifiers that adapt quickly to small patient cohorts, and knowledge-graph reasoning to connect sparse gene–variant–phenotype data. To enhance reliability in ultra-low-data settings, we integrate Bayesian uncertainty estimation, weak supervision, and generative augmentation, supported by a federated evaluation protocol that protects patient privacy. The framework addresses key challenges—such as domain shifts across populations, noisy variant labels, and sparse distributions—through structured risk analysis and mitigation. It is designed for three critical applications: (1) predicting variant pathogenicity, (2) prioritizing candidate genes from phenotypes, and (3) providing diagnostic support at the individual case level. By outlining reproducible methods, baseline models, and clinically meaningful evaluation metrics, this study demonstrates how few-shot approaches can accelerate equitable precision medicine in rare diseases where large datasets will never be available.