China's large-scale tree planting programs are critical for achieving its carbon neutrality by 2060, but determining where and how to plant trees for maximum carbon sequestration has not been rigorously assessed. Here, we developed a comprehensive machine learning framework that integrates diverse environmental variables to quantify tree growth suitability and its relationship with tree numbers. Then, their correlations with biomass carbon stocks were robustly established. Carbon sink potentials were mapped in distinct tree-planting scenarios. Under one of them aligned with China's ecosystem management policy, 44.7 billion trees could be planted, increasing forest stock by 9.6 ± 0.8 billion m³ and sequestering 5.9 ± 0.5 PgC equivalent to double China's 2020 industrial CO emissions. We found that tree densification within existing forests is an economically viable and effective strategy and so it should be a priority in future large-scale planting programs.