Hydrogen embrittlement in steels originates from complex interactions between hydrogen and crystalline defects, yet direct experimental observation remains difficult. Atomistic simulation can provide a direct, quantitative route to resolve these interactions, but its predictive capability critically depends on the accuracy and transferability of the underlying description of Fe–H interatomic interactions. In this work, we employ a Density Functional Theory (DFT)–trained machine‑learning interatomic potential—an artificial neural network (ANN) potential developed for the iron–hydrogen binary system [1–3]—to investigate hydrogen‑defect interactions governing deformation and fracture. This approach combines near‑DFT fidelity with the computational efficiency required for large‑scale molecular dynamics and systematic exploration of temperature, hydrogen content, and microstructural configurations. Our simulations elucidate how hydrogen modifies (i) vacancy thermodynamics and kinetics, including trapping and defect evolution; (ii) dislocation behavior, including hydrogen‑affected mobility and local plasticity; and (iii) grain boundary segregation and structural response, providing mechanistic links to both intragranular and intergranular fracture modes. By enabling consistent, atomistically grounded comparisons across multiple defect classes, the machine‑learning potential framework offers an integrated view of hydrogen embrittlement mechanisms and provides a basis for predictive modeling of hydrogen‑assisted failure in steels.