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This review systematically maps the scientific blind spots in the materials artificial intelligence literature by conducting a targeted search across key databases and journals to identify both what is heavily studied and what remains systematically invisible. The analysis organizes these blind spots into five interconnected categories—data, methods, evaluation, epistemic, and social—drawing on core peer-reviewed publications that collectively document the selective lens through which the field presents its progress. Key findings reveal that data blind spots center on underrepresented chemistries, structures, and operational conditions that leave critical real-world properties unmodeled; methodological blind spots arise from the dominance of correlation-driven approaches while uncertainty quantification, small-data techniques, and causal methods receive scant attention; evaluation blind spots manifest in the near-total absence of distribution-shift testing, robustness checks, and negative-result reporting that inflate perceived reliability; epistemic blind spots persist through prediction-without-explanation paradigms and the failure to articulate model boundary conditions or failure modes; and social blind spots ignore value-laden assumptions, equity considerations, and the broader societal and environmental implications of materials AI deployment. Synthesis across categories uncovers systemic patterns such as positive publication bias creating self-reinforcing feedback loops, methodological innovation consistently outpacing rigorous evaluation, data gaps mirroring decades-old research priorities, and epistemic shortcomings that propagate through every layer of the pipeline. Recommendations, therefore, target authors, reviewers, journals, and funders with concrete actions to surface these blind spots, thereby enabling a more balanced, reproducible, and societally relevant materials AI research agenda that closes the gap between published claims and real-world impact.