This study addresses the redundancy problem caused by an excessive number of components in Gaussian mixture models (GMMs) in practical applications, as well as the derivative issues such as overfitting and exponential growth of computational complexity, and proposes a component reduction method based on the GMM multi-scale mixture compression model (GMMultiMixer). Traditional GMM compression methods are limited by local optima, which can lead to model distortion and difficulty in handling complex multi-peak distributions. This paper draws on the multi-scale hybrid architecture and dynamic feature extraction capabilities of the TimeMixer++ model to propose the GMMultiMixer model for reconstructing the weights, means, and covariance parameters of GMM, thereby achieving optimal approximation of the original model. Experimental results demonstrate that this method significantly outperforms traditional strategies in terms of KL divergence metrics, particularly when fitting multi-modal, high-dimensional complex distributions, and it can also handle the compression task of two-dimensional GMM. Additionally, when combined with Kalman filtering for unmanned aerial vehicle (UAV) state estimation, this compression strategy effectively improves the system’s computational efficiency and state estimation accuracy.