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The integration of advanced computational tools with chemical process engineering has ushered in a new era of techno-chemistry, enabling data-driven optimization of complex chemical systems. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) techniques, has emerged as a transformative approach for enhancing catalyst design, reaction optimization, and process scale-up while aligning with sustainability objectives. This study presents a comprehensive techno-chemical framework for AI-assisted optimization of heterogeneous catalytic processes used in industrial chemical manufacturing. By combining experimental datasets, physicochemical descriptors, and process variables, predictive models were developed to optimize reaction yield, selectivity, energy efficiency, and environmental impact. The results demonstrate that AI-based models significantly outperform conventional trial-and-error methods, reducing experimental iterations, energy consumption, and waste generation. This research highlights the role of AI-driven techno chemical strategies in advancing sustainable industrial chemistry and provides a scalable pathway for next generation chemical manufacturing.