NdFeB (neodymium-iron-boron) permanent magnetic materials have been widely applied in modern industry and electronics due to their exceptionally high magnetic energy product and coercivity. However, in practical production, the compaction process— a critical stage in NdFeB manufacturing— still relies primarily on operator experience for setting process parameters. Variations in operator expertise and the inherent complexity of the production process often lead to unstable parameter settings, which adversely affect product quality and result in resource wastage. To accurately predict the process parameters during the powder compaction stage, this study proposes a Dynamic Layered Adjustment CatBoost (DLA-CatBoost) multi-output prediction model. Furthermore, an innovative hybrid optimization strategy, PSO-DSS-NSGA-III, which integrates particle swarm optimization to guide dynamic search space adjustment, is introduced to achieve multi-objective cooperative optimization of the model′s hyperparameters. Experimental results demonstrate that the DLA-CatBoost model optimized with the PSO-DSS-NSGA-III strategy exhibits excellent performance in multi-output prediction tasks, with a root mean square error (RMSE) ranging from 0.5 to 0.9, a mean absolute error (MAE) between 0.2 and 0.5, and a coefficient of determination (R2) between 0.96 and 0.99, thereby demonstrating its superior predictive capability and establishing it as an effective new approach for optimizing the process parameters in NdFeB compaction.