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The integration of artificial intelligence (AI) into materials science has evolved from basic data processing to sophisticated decision-making aids. Yet, a systematic conceptual model for transitioning from predictive to prescriptive functionalities remains underexplored. This paper develops a novel conceptual transition model for AI-enabled materials decision systems, emphasizing the interpretive dynamics and systemic interactions that facilitate this shift. Drawing on recent advancements in machine learning and data-driven methodologies, the model interprets how predictive AI, which forecasts material properties and behaviors, can extend into prescriptive AI, which recommends optimal actions for material design and engineering. Through a synthesis of theoretical backgrounds, we analyze the dynamics of interactions among data infrastructures, algorithmic processes, and human oversight, highlighting trade-offs among accuracy, interpretability, and scalability. Systems-level insights reveal feedback structures that enhance adaptability in complex materials environments, such as alloy development or nanomaterial synthesis. Ethical and epistemic reasoning underscores the need for transparent steering logics to mitigate biases and ensure reliable outcomes. The proposed framework offers analytical implications for materials engineers, guiding them in integrating AI to optimize decision-making without empirical validation. This conceptual approach contributes to a deeper understanding of AI’s role in advancing sustainable and efficient materials innovation.