Advanced foam materials featuring highly interconnected pore structures substantially improve heat and mass transfer performance in catalytic reactors and energy conversion systems, thereby facilitating process intensification that is both energy-efficient and aligned with carbon-neutral objectives. This book adopts a multidisciplinary methodology, integrating high-fidelity computational modeling, physics-informed machine learning, and experimentally validated characterization, to address persistent technical challenges across catalytic reactors, proton-exchange membrane fuel cells, solar-thermal energy systems, and low-emission combustion technologies. Key methodological and technological contributions include: (i) The key applications of foam structures in the field related to energy; (ii) AI-enhanced porous media modeling for predictive optimization of transport processes; and (iii) thermally adaptive design strategies for safe and efficient management of highly exothermic processes, including CO₂ methanation and hydrogen-blended combustion. Grounded in application-driven research, the work demonstrates quantifiable advances in emission reduction, system-level energy efficiency gains, and scalable implementation pathways.