This work explains an integrated approach for the analysis and optimization of catalytic systems based on systematic analysis, physical-mathematical modeling, and modern computer and information technologies. A hybrid computational model is introduced which combines classical physical-chemical modeling methods with variational quantum algorithms, such as Variational Quantum Eigensolver, Variational Quantum Classifier, and Grover's quantum search algorithm. This strategy is designed to reduce the combinatorial complexity of the design space for catalytic materials and improve the accuracy of assessing their energy and functional properties. Quantum-classical methods allow for subtle quantum effects missing from traditional digital models; even more importantly, they retain computational feasibility on existing Noisy Intermediate-Scale Quantum devices. These results demonstrate the potential for integrating quantum computing into sustainable development goals related to energy-saving technologies, resource cost reduction, and the development of environmentally friendly catalytic processes.