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The limited transferability of polygenic scores (PGS) across populations constrains their clinical utility and risks exacerbating health disparities, given challenges in multi-ancestry training, fine-mapping, and variant prioritization using genomic annotations, particularly when biologically relevant reference resources are sparse or unavailable for the target population. Here, we introduce PRISM, a transfer learning approach that jointly addresses these challenges to enhance PGS transferability. Applying PRISM to 7352 fine-mapped variants, 414 ENCODE annotations, and 406,659 individuals from the UK Biobank, we demonstrate that ancestry-aware integration of tissue-specific annotations yields the largest gains in predictive performance for African ancestry, with an average improvement of 13.10% (p=1.6×10) over annotation-agnostic multi-ancestry PGS. Notably, the best-performing model uses 102-fold fewer annotations than non-specific models, with contributions from broad categories of annotations. Overall, PRISM complements ongoing data diversification efforts by providing an immediately applicable strategy based on the integration of biologically aligned, best-available resources to address genomic health equity.