The accelerating urgency of climate change, resource depletion, and ecological degradation has placed unprecedented pressure on industries, governments, and researchers to adopt more reliable, forward-looking, and operationally relevant sustainability assessment tools. Life cycle assessment (LCA), standardized through ISO 14044, has long served as the methodological backbone for evaluating environmental impacts across product and process life cycles, yet its traditional reliance on static inventories, linear modeling assumptions, and data-intensive workflows has increasingly limited its ability to address complex, rapidly evolving technological systems (ISO, 2006). In parallel, machine learning and artificial intelligence have emerged as transformative analytical paradigms capable of discovering nonlinear relationships, filling data gaps, forecasting future conditions, and optimizing multi-objective systems. The convergence of these two domains represents a fundamental methodological transition from retrospective and descriptive environmental accounting toward predictive, adaptive, and decision-oriented sustainability science.

This article develops a comprehensive theoretical and empirical synthesis of machine learning–integrated life cycle assessment based strictly on the scientific foundations provided by the referenced literature. Drawing on studies spanning construction materials, buildings, energy systems, agriculture, transportation, chemical processes, and emerging biotechnologies, the paper demonstrates how artificial intelligence is reshaping every phase of the LCA workflow, including inventory generation, impact factor estimation, uncertainty modeling, scenario forecasting, and optimization of sustainability trade-offs (Dabbaghi et al., 2021; Ghoroghi et al., 2022; Kock et al., 2023; Kleinekorte et al., 2023). Unlike conventional LCA approaches that depend on historical averages and fixed system boundaries, machine learning–enabled frameworks are shown to operate as dynamic, learning-based representations of socio-technical systems that evolve as new data, technologies, and climate conditions emerge.

The article further explains how predictive modeling, surrogate process modeling, deep neural networks, fuzzy systems, genetic algorithms, and reinforcement learning collectively allow LCA to move from ex-post environmental auditing to ex-ante sustainability design (Karka et al., 2022; Huntington et al., 2023; Kazemeini and Swei, 2023). Empirical evidence from sectors such as concrete production, bioenergy, crop cultivation, vehicle manufacturing, and carbon capture illustrates that AI-enhanced LCA can drastically improve both accuracy and decision relevance by capturing nonlinear process behavior, regional variability, and long-term uncertainty (Kaab et al., 2019; Lee et al., 2020; Javadi et al., 2021; Dong and Zhang, 2023). Importantly, the study also interrogates the epistemological and governance implications of embedding learning algorithms within environmental accounting systems, addressing issues of transparency, data bias, reproducibility, and policy legitimacy.

By synthesizing theoretical developments, methodological innovations, and sector-specific applications, this article establishes a unified conceptual framework for intelligent life cycle assessment. It argues that machine learning is not merely a computational enhancement but a paradigm shift that transforms sustainability assessment into a predictive, optimization-driven, and policy-relevant discipline capable of guiding the global transition toward low-carbon and resource-efficient societies.