<p>The asymmetric hydrogenation of olefins is one of the most important asymmetric transformations in molecular synthesis. While other machine learning models have successfully predicted stereoselectivity for reactions with a single prochiral site, existing models face limitations including narrow substrate–catalyst applicability, an inability to simultaneously predict stereoselectivity and absolute configurations in asymmetric hydrogenation of olefins with two prochiral sites, and a reliance on predefined descriptors. Here, to overcome these challenges, we introduce Chemistry-Informed Asymmetric Hydrogenation Network (ChemAHNet), a deep learning model based on the reaction mechanism of olefin asymmetric hydrogenation. By leveraging three structure-aware modules, ChemAHNet accurately predicts the absolute configuration of major enantiomers across diverse catalysts and substrates. It also defines the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\mathrm{\varDelta \varDelta }{G}^{{\boldsymbol{\ddagger }}}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>ΔΔ</mi> <msup> <mrow> <mi>G</mi> </mrow> <mo>‡</mo> </msup> </mrow> </math></EquationSource> </InlineEquation> of asymmetric hydrogenation via catalyst–olefin interactions, enabling concurrent prediction of stereoselectivity and absolute configuration. Notably, ChemAHNet extends to other asymmetric catalytic reactions. By operating solely on simplified molecular-input line-entry system inputs, it captures atomic-level spatial and electronic interactions, offering a robust tool for target-directed molecular engineering.</p>