Electricity consumption prediction is an important part of power planning and the basis of power dispatch planning. The SVM and GBDT algorithms that were optimized by the PSO algorithm are used to build the machine learning-based electricity consumption prediction model in this paper. The decomposed and reconstructed high-frequency signals and low-frequency signals are optimized by the particle swarm algorithms SVM and GBDT for power consumption prediction, respectively. Improve the clustering performance of the traditional fuzzy C-mean algorithm for unbalanced data. The performance of the algorithms is analyzed in two different application scenarios, namely, artificial dataset and power users' real measurement dataset. Power consumption prediction in the manufacturing industry is performed. It is found that the ARI, FMI, and AMI index values of the improved algorithms proposed in this paper are 0.9543, 0.9347, and 0.9344, respectively, on the grid user-measured dataset, while the traditional DPC and K-means clustering algorithms are less effective. The machine learning algorithm optimized after wavelet decomposition increased R² by 8.98%, MAPE decreased by 19.78%, and RMSE decreased by 11.53% compared to PSO-GBDT, and the predictive evaluation indexes were all improved, and the two machine learning algorithms in this paper optimized by wavelet decomposition combined with PSO have good predictive effect R² increased from 0.801 to 0.842, and the two machine learning algorithms designed in this paper based on wavelet decomposition have good predictive effect R² increased from 0.801 to 0.842. The designed machine learning model for electricity consumption prediction based on wavelet decomposition and PSO has excellent performance, and the design expectation has been fulfilled. This paper makes a useful exploration and proposes an effective method for accurate prediction of electricity consumption in the manufacturing industry.