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Foundation models and large language models (LLMs) are rapidly entering materials science, offering new interfaces for property prediction, synthesis planning, and literature mining. This review synthesizes some peer-reviewed publications, focusing on foundation models pre-trained on crystals, molecules, and text, as well as LLM-based approaches for materials tasks. Three primary use cases emerge: (1) property prediction from compositional or structural descriptions, (2) synthesis recipe generation, and (3) extraction of structured data from scientific text. Methods span zero-shot prompting, few-shot prompting, chain-of-thought reasoning, retrieval-augmented generation, full fine-tuning, parameter-efficient fine-tuning, and embedding-based adaptation. What transfers effectively includes generic chemical knowledge, structure–text relationships, qualitative trends, similarity search, and literature extraction. In contrast, what does not transfer includes quantitative property prediction to experimental accuracy, extrapolation beyond pre-training distributions, crystal stability assessment, physics-based reasoning, and hallucination-free synthesis planning. Despite promising demonstrations in common materials, LLMs and foundation models still lag behind specialized graph neural networks in quantitative tasks and fail on compositional or structural novelty. This review provides a systematic taxonomy of applications, a critical analysis of prompting and fine-tuning strategies, and a clear delineation of transfer limitations. Gaps remain in uncertainty quantification, multimodal data scarcity, and rigorous benchmarking against non-LLM baselines. Recommendations for practitioners and developers emphasize realistic expectations and hybrid human–AI workflows to accelerate materials discovery without over-reliance on ungrounded predictions.