<p>Aiming at the insufficient prediction accuracy caused by the non-stationary and multi-frequency coupling characteristics of complex multi-source vibration signals, this study proposes a parallel fusion prediction model that takes hyperparameter optimization as the core preprocessing step and integrates multivariate variational mode decomposition (MVMD) with parallel weighted KNN-XGBoost. Firstly, a “training-validation-test” three-set data separation framework is constructed, and a stepwise grid search strategy is adopted to optimize key hyperparameters. With the error validation set as the objective function, the number of neighbors for K-Nearest Neighbors (KNN), the tree depth and learning rate for eXtreme Gradient Boosting (XGBoost), and the optimal fusion weights of KNN and XGBoost are determined sequentially. This fundamentally avoids the subjective bias inherent in manual parameter tuning. Secondly, MVMD is employed to decompose complex multi-source vibration signals into 8 intrinsic mode functions (IMFs) with different frequency scales. This addresses the mode mixing issue of traditional decomposition methods, reduces the complexity of vibration signals, and retains the multi-source coupling features. Thirdly, a parallel weighted KNN-XGBoost structure is built based on the optimized parameters. Through independent prediction and weighted fusion, error propagation in serial structures is avoided, enabling the synergy of “local correction-global fitting”. Multiple comparative and ablation experiments demonstrate that, compared with other models, the MVMD-parallel-KNN-XGBoost model achieves the optimal balanced performance. This study provides a parallel fusion technical pathway of “parameter optimization-modal separation-model fusion” for complex multi-source vibration time-series prediction, which can meet the high-precision prediction requirements of complex multi-source vibration in fields such as aerospace, rail transit, and industrial engineering etc.</p>