Despite extensive efforts to sequence different genomes, genetic models to interpret gene regulation and cell fate decisions are lacking for most species. Here, we performed whole-body single-cell transcriptomic analysis of zebrafish, Drosophila, and earthworm. We then mapped cell landscapes covering eight representative metazoan species to study gene regulation through evolution. With uniformly constructed cross-species datasets, we developed a deep learning-based strategy, Nvwa, to predict gene expression landscapes and decipher cis-regulatory elements (CREs) at the single-cell level. We systematically compared cell type-specific transcription factors (TFs) and CREs to reveal conserved genetic regulation among vertebrates and invertebrates. Our work provides a valuable resource and a novel strategy for studying regulatory language in diverse biological systems.