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Large single-cell ribonucleic acid-sequencing (scRNA-seq) datasets offer unprecedented biological insights but present substantial computational challenges for visualization and analysis. While existing subsampling methods can enhance efficiency, they may not ensure optimal performance in downstream machine learning and deep learning (ML/DL) tasks. Here, we introduce scValue, a novel approach that ranks individual cells by ‘data value’ using out-of-bag estimates from a random forest model. scValue prioritizes high-value cells and allocates greater representation to cell types with higher variability in data value, effectively preserving key biological signals within subsamples. We benchmarked scValue on automatic cell-type annotation tasks across four large datasets, paired with distinct ML/DL models. Our method consistently outperformed existing subsampling methods, closely matching full-data performance across all annotation tasks. In three additional case studies—label transfer learning, cross-study label harmonization, and bulk RNA-seq deconvolution—scValue more effectively preserved T-cell annotations across human gut-colon datasets, more accurately reproduced T-cell subtype relationships in a human spleen dataset, and constructed a more reliable single-cell immune reference for cell-type deconvolution in simulated bulk tissue samples. Finally, using 16 public datasets ranging from tens of thousands to millions of cells, we evaluated subsampling quality based on computational time, Gini coefficient, and Hausdorff distance. scValue demonstrated fast execution, well-balanced cell-type representation, and distributional properties akin to uniform sampling. Overall, scValue provides a robust and scalable solution for subsampling large scRNA-seq data in ML/DL workflows. It is available as an open-source Python package installable via pip, with source code at  https://github.com/LHBCB/scvalue .

scValue: value-based subsampling of large-scale single-cell transcriptomic data for machine and deep learning tasks