In this work, we introduced kQEq, a kernel-based approach to charge equilibration in molecules. In contrast to conventional charge equilibration methods like QEq, a data-driven, environment-dependent description of atomic electronegativities is introduced. kQEq models trained on molecular dipole moments display excellent performance, en par with or better than state-of-the-art kernel models, specifically tuned to predicting dipole moments. The formalism presented herein opens the door towards physics-based kernel ML models for predicting atomic charges, to be used in combination with reactive interatomic potentials. Importantly, the presented approach is quite general and can be extended to other fit targets (e.g quadrupole moments and electrostatic potentials) and to more flexible density representations (e.g using atom centered dipoles in addition to partial charges).