Generative AI for Sustainable Product Design: A Technology Convergence Framework Integrating Multi-Objective Optimisation and Smart Manufacturing

The accelerating adoption of generative artificial intelligence (AI) is reshaping sustainable product design, yet current research remains fragmented across computational design, multi-objective optimisation, and smart manufacturing. This systematic review addresses this fragmentation by analysing 59 peer-reviewed studies (2010–2025) using PRISMA guidelines, advanced bibliometric mapping, and structural topic modelling to uncover how these domains converge to create superior sustainability outcomes. The study develops the Technology Convergence Framework, a unified theoretical model that integrates Advanced Computational Methods, Multi-Objective Optimisation, and Smart Manufacturing into an interconnected system capable of delivering emergent performance improvements. Findings show that when these domains operate synergistically—supported by mechanisms such as infrastructural maturation, empirical validation feedback loops, and standardisation-driven diffusion—manufacturers achieve 30%–65% gains in energy efficiency, waste reduction, and material optimisation, far exceeding improvements achieved through isolated technological efforts. The framework further incorporates human-AI collaboration principles aligned with Industry 5.0, emphasising the critical role of human judgement, contextual reasoning, and ethical oversight in complementing AI-driven decision systems. By bridging methodological, technological, and operational gaps, this review provides a holistic roadmap for transitioning from fragmented innovation to integrated sustainable product realisation, offering both scholars and industry leaders a coherent foundation for advancing next-generation sustainable manufacturing ecosystems.