With the rapid and transformative advances of large language models (LLMs) in natural language processing, the capabilities of these models in knowledge integration and reasoning have opened new technological pathways for intelligent industrial applications. This review systematically surveys key adaptation techniques, representative application scenarios, and future development trends of LLMs in industrial scenarios. It also provides an integrated overview of their application paradigms and technical characteristics across core industrial processes. Key adaptation techniques for industrial scenarios are first analyzed, including prompt engineering, retrieval-augmented generation (RAG), and parameter-efficient fine-tuning, together with a summary of commonly used evaluation metrics and LLM-based assessment approaches. Representative practices of LLMs are then systematically reviewed across the product lifecycle, covering product design, process planning, production and manufacturing, as well as operation and maintenance. The effectiveness of LLMs in addressing practical industrial problems, facilitating technological innovation, and improving application performance is examined. Finally, major challenges currently encountered in industrial applications of LLMs are identified, including the scarcity of high-quality datasets, limited multimodal fusion capability, insufficient domain specificity, reliability concerns, constrained interpretability, and the lack of standardized evaluation frameworks. Corresponding future research directions are outlined, such as the development of data augmentation and secure sharing mechanisms, the exploration of novel model architectures, and the establishment of intelligent evaluation systems. Overall, this review provides a comprehensive reference for systematic investigations of LLM applications across the entire industrial process and offers theoretical foundations and methodological guidance for both academic research and engineering practice.