Accurate spectral prediction of CMYK printed images is critical for ensuring color reproduction fidelity in modern printing processes. Traditional physical models, such as the Murray-Davies and Yule–Nielsen models, have constraints in capturing the complexity of ink interactions and the contribution of black ink, resulting in decreased spectral prediction accuracy. To address these challenges, a novel multi-output weighted support vector regression (MO-WSVR) model for multispectral reconstruction of CMYK printed images is proposed. By modeling multiple spectral bands cumulatively, MO-WSVR is capable of predicting a range of spectral points simultaneously. Furthermore, the model incorporates a dynamic weighting mechanism that assigns greater weights to spectral points with higher prediction errors, enabling the model to better accommodate the inherent characteristics of CMYK printed images. Experimental results demonstrated that the MO-WSVR model significantly outperformed traditional physical models and existing data-driven prediction methods, based on root mean square error (RMSE) and colorimetric accuracy (CIEDE2000).