Traditional two-dimensional inversion methods of magnetotelluric are mature, but there are still some problems, such as reliance on the initial model, reliance on regularization parameter selection, and easy to fall into local minimum. In order to solve the above problems, this paper adopts the supervised descent method to improve the effect of two-dimensional inversion of magnetotelluric. The supervised descent method is a machine learning algorithm that learns the average descending direction to predict the residual of data. Based on the theory of supervised descent method, this paper develops the two-dimensional inversion algorithm of magnetotelluric, designs the theoretical model synthesis example to verify the correctness of the algorithm, and inverts the measured data on the Tibet Plateau to test the practicability of the supervised descent method. The inversion results of the theoretical model synthesis data and the measured data show that, compared with the traditional nonlinear conjugate gradient inversion, the inversion based on the supervised descent method has the characteristics of fast convergence speed, good inversion effect, and strong anti-noise ability.