Abstract: The construction of accurate enzyme-constrained genome-scale models (ecGEMs) remains a critical challenge in systems biology, limited by sparse kinetic data and the need for biologically meaningful representations. This work presents an integrated framework combining CPI-Pred, a deep learning model to predict kinetic parameters ($k_{cat}$, $K_M$, $K_I$, and $k_{cat}$/$K_M$) from sequence and compound embeddings, with kinGEMs, a pipeline to incorporate these parameters into ecGEMs for metabolic optimization. By leveraging representations at multiple scales, the approach captures sequence, structure, and kinetic data to enhance model generalizability and accuracy. Rigorous benchmarking demonstrates the framework's capability to predict growth rates and fluxes that are consistent with experimental observations, reduce median flux variability by 3 fold, and enable better-defined predictive and interpretable metabolic models. These innovations open new avenues for metabolic engineering and synthetic biology, offering robust tools to explore biological perturbations and guiding experimental designs.