As far as we know, there is no flat-histogram algorithm to sample the stationary
distribution of non-equilibrium stochastic processes. The present work addresses this gap by
introducing a generalization of the Wang-Landau algorithm, applied to non-equilibrium
stochastic processes with local transitions. The main idea is to sample macroscopic states using a
kinetic Monte Carlo algorithm to generate trial moves, which are accepted or rejected with a
probability that depends inversely on the stationary distribution. The stationary distribution is
refined through the simulation by a modification factor, leading to convergence toward the true
stationary distribution. A visitation histogram is also accumulated, and the modification factor is
updated when the histogram satisfies a flatness condition. The stationary distribution is obtained
in the limit where the modification factor reaches a threshold value close to unity. To test the
algorithm, we compare simulation results for several stochastic processes with theoretically
known behavior. In addition, results from the kinetic flat-histogram algorithm are compared with
standard exact stochastic simulations. We show that the kinetic flat-histogram algorithm can be
applied to phase transitions in stochastic processes with bistability, which describe a wide range
of phenomena such as epidemic spreading, population growth, chemical reactions, and
consensus formation. With some adaptations, the kinetic flat-histogram algorithm can also be
applied to stochastic models on lattices and complex networks.