Semantic search has changed as a result of the advancement of Natural Language Processing (NLP) and the emergence of Large Language Models (LLMs) like BERT, GPT, and T5. Conventional search engines mostly use keyword-based matching, which frequently produce results that are erroneous or unrelated. Semantic search, on the other hand, aims to comprehend the underlying meaning and context behind searches and documents, going beyond keyword matching. This research explores how LLMs enhance semantic search by utilizing these models, which are trained on large text corpora. They produce contextual embeddings that record the connections between words, facilitating more accurate and pertinent search results as well as a deeper comprehension of text semantics. The integration of LLMs into semantic search systems is thoroughly examined in this work, with a focus on the mechanisms that improve search accuracy. To increase search relevancy, LLMs employ vector-based similarity metrics, contextual representations, and token embedding. Particularly in specialized domains like healthcare, law, and finance, search systems can produce more accurate and contextually relevant results by combining the structured data from knowledge graphs with the contextual understanding offered by LLMs. This study highlights how LLMs might improve semantic search while simultaneously tackling the main obstacles to their use. We suggest avenues for further research, such as methods for boosting the real-time performance of search systems for large-scale applications, reducing biases, and increasing model efficiency. It is anticipated that as LLMs advance, they will be essential to the creation of intelligent search systems of the future. By giving consumers more human-like interactions and improving search engine efficacy across a range of areas, these technologies will provide more precise, context-aware, and adaptable search experiences.