Efficiently allocating fish catch to appropriate production lines is vital for maximizing economic value in the seafood industry. This paper proposes a machine learning-based framework for non-destructive fish-to-product classification using vibrational spectroscopy techniques. Instead of predicting accurate biochemical compositions, the allocation task is formulated as a multi-class classification problem, with classes derived from expert-driven clustering of biochemical profiles. To address the challenges posed by noisy and limited spectral data, a novel data processing framework is proposed, which integrates data augmentation, feature selection and feature fusion. This framework enriches the training dataset through domain-inspired linear spectral augmentation and employs selective feature fusion to extract robust and complementary features from multiple spectral modalities. The resulting fused features are then used to train standard classifiers, leading to improved classification performance. Experimental results on real-world fish spectral datasets demonstrate the effectiveness of this approach, offering a practical tool for intelligent production line allocation in fish processing.