This paper introduces an inexact two-level smoothing optimization framework (ItsOPT) for finding first-order critical points of nonsmooth and nonconvex functions. The framework involves two levels of methodologies: at the upper level, a first- or second-order method will be tailored to minimize a smooth approximation of the cost function; at the lower level, the high-order proximal auxiliary problems will be solved inexactly. As a smoothing technique, in particular, we here introduce the high-order Moreau envelope (HOME) and study its fundamental features under standard assumptions and its differential properties under a variant of prox-regularity. Next, introducing a high-order proximal-point algorithm (HiPPA) and its boosted variant (Boosted HiPPA) at the upper level and solving the proximal subproblem inexactly at the lower level lead to an instance method of the ItsOPT framework. Global and linear convergence results are established under the Kurdyka-Łojasiewicz (KL) property of the cost and envelope functions, along with some reasonable conditions for the accuracy of the proximal terms. Preliminary numerical experiments on a robust low-rank matrix recovery problem indicate a promising performance of the proposed algorithm, validating our theoretical foundations.