In laser systems, it is well known that beam pointing is shifted due to many un-modeled factors, such as vibrations
from the hardware platform and air disturbance. In addition, beam-pointing shift also varies with laser sources as
well as time, rendering the modeling of shifting errors difficult. While a few works have addressed the problem of
predicting shift dynamics, several challenges still remain. Specifically, a generic approach that can be easily applied
to different laser systems is highly desired. In contrast to physical modeling approaches, we aim to predict
beam-pointing drift using a well-established probabilistic learning approach, i.e., the Gaussian mixture model. By
exploiting sampled datapoints (collected from the laser system) comprising time and corresponding shifting
errors, the joint distribution of time and shifting error can be estimated. Subsequently, Gaussian mixture
regression is employed to predict the shifting error at any query time. The proposed learning scheme is verified
in a pulsed laser system (1064 nm, Nd:YAG, 100 Hz), showing that the drift prediction approach achieves
remarkable performances.