Machine learning (ML) provides a promising route for predicting the low‐temperature impact toughness of thermo‐mechanical controlled processing (TMCP) microalloyed steels, yet data noise and microstructure‐dependent mechanisms remain major challenges. In this study, several thousand industrial records were cleaned, and a three‐stage feature‐selection procedure was applied to construct an optimized Random Forest model, raising the test‐set coefficient of determination ( R 2 ) from 0.55 to 0.81. SHAP (SHapley Additive exPlanations) analysis identifies Finish rolling reduction (FRR), Second‐stage rolling temperature (SRT), C, Nb, and Rolling pass (RP) as the dominant factors controlling impact energy. Three representative steels were further examined to validate the ML interpretation, with prediction errors within 20–30 J at −20°C. Microstructural analyses show that coarse NbTi(C, N) precipitates intensify dislocation interactions and void nucleation, while higher SRT promotes recovery and the formation of equiaxed ferrite grains, thereby enhancing crack deflection. EBSD confirms that crack propagation is governed by local lattice rotation and slip‐system activation. The integrated ML–microstructure approach provides quantitative insight and practical guidance for toughness‐oriented alloy and process design.