  We derive an explicit form of the dilaton potential in improved holographic
QCD (IHQCD) from the QCD lattice data of the chiral condensate as a function of
the quark mass. This establishes a data-driven holographic modeling of QCD --
machine learning holographic QCD. The modeling consists of two steps for
solving inverse problems. The first inverse problem is to find the emergent
bulk geometry consistent with the lattice QCD simulation data at the boundary.
We solve this problem with the refinement of neural ordinary differential
equation, a machine learning technique. The second inverse problem is to derive
a bulk gravity action with a dilaton potential such that its solution is the
emergent bulk geometry. We solve this problem at non-zero temperature, and
derive the explicit form of the dilaton potential. The dilaton potential
determines the bulk action, the Einstein-dilaton system, thus we derive
holographically the bulk system from the QCD chiral condensate data. The
usefulness of the model is shown in the example of the prediction of the string
breaking distance, whose value is found to be consistent with another lattice
QCD data.
