Wind-induced effects should be especially concerned for the design and maintenance of high-rise buildings, and information of wind load is sometimes prerequisite for related wind-resistant practices. Previous studies have been mainly conducted to deduce wind load on high-rise buildings under stationary and Gaussian conditions. However, due to the inherent randomness, wind load often exhibits non-stationary and non-Gaussian characteristics. This study employs two methods, namely Discrete Kalman Filter (DKF) and Fast Bayesian Fourier Transform (FB-FFT), to estimate wind load on high-rise buildings under non-stationary and/or non-Gaussian conditions. Firstly, a numerical building model is employed to evaluate the inversion performance of the two methods. Subsequently, both methods are applied to determine the wind load on a real building. Results through numerical simulation analysis demonstrate that DKF can invert the base force and modal force accurately under non-stationary conditions. For stationary non-Gaussian cases, the inverted wind load approximates the target force in terms of amplitude, but the inversion result exhibits Gaussian characteristics. FB-FFT can be basically utilized to invert the modal force under varied concerned conditions, yet the estimated wind load for stationary non-Gaussian case is slightly smaller than the target value. In reference to the real high-rise building example, DKF is found to produce similar results to those determined on the basis of wind tunnel test and in-situ measurement. Meanwhile, results of model force obtained via the two methods agree with each other. The above findings suggest that DKF and FB-FFT can be employed, at least partially, to deal with the issues of wind load inversion under non-stationary and non-Gaussian conditions.