Non-thermal plasma-nanoparticle interactions can modify reaction kinetics through synergistic effects resulting from the interplay between various plasma-excited species and nanoparticles. Identifying the contributions of each plasma-excited species, such as vibrationally excited species, surface charges, and reactive radicals, to the overall catalytic performance is challenging due to the lack of high-resolution in-situ spectroscopy methods. Therefore, the primary objective of this proposed research is to understand and quantitatively determine how interactions between non-thermal plasma components (hot electrons, reactive radicals, vibrationally excited species) and surface reactions influence the activity and selectivity of desired reactions. This will be achieved by developing a novel multi-scale model informed deep learning algorithm. To explore the impact of plasma-nanoparticle interactions on enhancing reaction kinetics, the project will focus on clean hydrogen production using non-critical metallic nanoparticles, a crucial factor for decarbonization. Specifically, the aim will be to develop multi-scale model-informed deep learning methods to: (i) investigate the charge distribution of metallic nanoparticles under non-thermal plasma conditions; (ii) quantify the effects of vibrationally excited species and radical interactions with charged nanoparticles on the activity of plasma catalysis; and (iii) identify the optimal plasma-nanoparticle interaction effects on catalysis. This research will provide a fundamental understanding of how plasma-nanoparticle interactions modify plasma kinetics, thereby improving energy efficiency for decarbonization and sustainability. Additionally, the multi-scale model-informed deep learning algorithm will expedite the advancement of low-temperature plasma chemistry and material discovery.