Black carbon's climate impact is highly uncertain because its radiative effect depends on particle size, mixing state, and column loading.  This study combines satellite data, physics-based simulations, and machine learning to estimate black carbon forcing across contrasting regions. The same black carbon amount can warm or cool the atmosphere depending on local aerosol properties. The machine learning framework provides a fast, transferable tool for regional climate assessment.