Predicting the structures of chemical and biomolecular products from reactants is a fundamental challenge in organic chemistry and biochemistry, essential for advancing synthetic pathway design and drug discovery. Traditional methods often fail to capture the intricate, multi-scale dynamics of these transformations, resulting in limited predictive accuracy and interpretability. In this work, we introduce ChemTransGNN +  +, a novel machine learning framework that integrates Graph Neural Networks (GNNs) and Transformers to predict chemical and biomolecular products from reactants with unprecedented accuracy. ChemTransGNN +  + incorporates four innovative components: Multi-Scale Chemical Attention (MSCA), which captures transformation patterns at atomic, functional group, and molecular levels; Reaction Narrative Flow (RNF) and Dynamic Graph Rewriting with Narrative Memory (DGR-NM), which model the temporal evolution of molecular graphs for chemically consistent predictions; and Visual Narrative Generator (VNG), which enhances interpretability by visualizing reaction pathways and key substructures. Evaluated on a dataset of 50,000 reaction pairs, ChemTransGNN +  + achieves state-of-the-art (SOTA) performance, with a Top-1 SMILES prediction accuracy of 87.3%, a Tanimoto similarity of 0.85, and a chemical validity of 96.8%, surpassing established methods such as Molecular Transformer and ReaMVP by 1.4% in accuracy and 1.7% in chemical validity. An ablation study validates the contributions of each component, while visualizations demonstrate the model’s ability to predict and interpret diverse reaction mechanisms, from dehydrations to cyclizations, across chemical and biomolecular systems. These results position ChemTransGNN +  + as a powerful tool for reaction product prediction, offering both high accuracy and mechanistic insights for applications in organic chemistry and biochemistry.