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The integration of artificial intelligence (AI) and machine learning (ML) in materials science has revolutionized traditional approaches to material discovery, design, and application. This narrative review explores how AI models not only predict material properties but also influence scientific decision-making by providing actionable insights, optimizing experimental strategies, and enabling inverse design paradigms. Drawing on recent advancements, we examine the transition from data-driven prediction to AI-assisted decision-making, highlighting case studies in porous materials, optoelectronics, and polymeric membranes. The review addresses challenges such as data scarcity, model interpretability, and integration with experimental workflows, while proposing future directions for AI to enhance human decision-making in materials research. Ultimately, AI is positioned as a collaborative tool that augments scientific intuition, accelerating innovation in sustainable and high-performance materials.

The rise of artificial intelligence (AI) in materials science has highlighted a profound epistemic tension. While AI models excel in predictive accuracy, they often fail to provide mechanistic insights into materials behavior, raising questions about whether such predictions constitute genuine scientific understanding. This tension is particularly acute in materials science, where complex phenomena like phase transitions, defect dynamics, and property emergence demand not only forecasting but also explanatory depth to inform reliable design and innovation. Equating prediction with understanding risks epistemic overreach, potentially leading to unwarranted confidence in AI outputs and hindering progress in fields requiring causal knowledge, such as sustainable materials development. This paper proposes a novel theoretical framework that redefines “understanding” in AI-driven materials research as a multi-layered epistemic construct, distinguishing predictive success from mechanistic insight and actionable knowledge. The framework introduces epistemic validity conditions, interpretive constraints, and decision contexts for evaluating AI contributions, emphasizing alignment with physical principles and the avoidance of semantic inflation. By synthesizing recent literature, it addresses conceptual gaps in current approaches and advocates responsible inference that integrates predictive power with explanatory rigor. This contribution advances philosophical foundations for AI in materials science, fostering more robust, trustworthy scientific practices without empirical validation claims.