Volume 1 - Issue 4, November - December 2025

The escalating global environmental crisis necessitates a paradigm shift in product development, moving from reactive impact assessment to proactive environmental forecasting. Traditional Life Cycle Assessment (LCA) is the established standard for quantifying a product's environmental footprint, yet its complexity, dataintensive nature, and tendency to be applied late in the design process limit its utility for real-time optimization. This paper explores the transformative potential of AI-Powered Predictive Sustainability, a novel framework that integrates machine learning (ML) algorithms with comprehensive LCA databases to forecast environmental impacts based on preliminary design and material specifications. By leveraging predictive models such as Artificial Neural Networks (ANNs) and Gaussian Process Regression (GPR), this approach enables designers and engineers to simulate and optimize a product's environmental performance—including carbon footprint, water usage, and resource depletion—before any physical manufacturing takes place. The research outlines a conceptual methodology for developing and validating such a system, emphasizing the critical role of big data in training robust models. The findings suggest that AI-powered predictive sustainability offers a scalable, rapid, and highly accurate mechanism for front-loading environmental responsibility, thereby accelerating the transition toward a truly circular and sustainable economy.