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This review systematically examines conceptual approaches to uncertainty communication in materials artificial intelligence, synthesizing insights from 35 peer-reviewed publications published between 2017 and 2025 that span uncertainty quantification techniques, visualization strategies, human-factors research, and domain-specific applications in computational materials science. The methodology involved targeted searches across Web of Science, Scopus, arXiv, and PubMed using strings such as “uncertainty communication” machine learning, “uncertainty visualization” materials AI, “predictive uncertainty” materials informatics, and related terms, with strict inclusion criteria limited to English-language peer-reviewed works that explicitly address the reporting, visualization, or human interpretation of uncertainty estimates, yielding a final corpus of 35 core references after PRISMA-style screening. Foundations of uncertainty communication are drawn from risk-communication literature and cognitive science, emphasizing that effective transmission of predictive uncertainty is essential for building trust and enabling sound decision-making. Yet, it remains distinct from mere quantification because users frequently misinterpret or ignore numerical confidence measures when they lack contextual framing. Current practices in materials AI reveal a persistent gap: while uncertainty quantification is increasingly present through confidence intervals or ensemble variances, explicit communication to end users—whether fellow researchers or industrial decision-makers—is rare, often limited to parenthetical standard deviations or simple error bars that fail to convey epistemic versus aleatoric components or their implications for downstream materials design. Approaches to uncertainty communication surveyed here encompass numerical, visual, verbal, interactive, and decision-focused modalities, each evaluated for strengths and limitations when applied to high-stakes materials predictions. Materials-specific challenges, including multi-scale propagation and costly experimental validation, exacerbate these issues, leading to identified gaps such as the absence of standardized reporting guidelines and limited empirical studies on user understanding; the review concludes with actionable recommendations for authors, journals, reviewers, and the broader community to elevate uncertainty communication from an afterthought to a core pillar of responsible materials AI.