Deep Learning for Sustainable Product Design: Shuffle-GhostNet Optimized by Enhanced Hippopotamus Optimizer to Life Cycle Assessment Integration

The intelligence of sustainable design is reflected in the demands for accurate and real-time environmental impact assessments; traditional LCA methods are slow and static. In this paper, we propose a novel deep learning framework that serially links Shuffle-GhostNet (a lightweight convolutional neural network employing a combination of Ghost and Shuffle modules) improved by an enhanced version of Hippopotamus Optimizer (EHHO) for hyperparameter tuning and enhanced convergence. Upon testing the model on the Ecoinvent and OpenLCA Nexus datasets, pronounced advantages in predicting CO2 emissions, energy use, and other sustainability indicators were found. Coupling the integration of multi-source sensor data and optimizing the architecture via metaheuristic search enables rapid and reliable decision support on eco-design. Final results are significantly better than the baseline models, achieving an R2 of up to 0.943 with actual performance gains. AI-driven modeling integrated with LCA constitutes a pathway toward dynamic and scalable sustainability assessment in Industry 4.0 and circular economy applications.