The rapid growth of data in chemical food laboratories necessitates efficient and scalable retrieval methods. This paper evaluates the performance of a semantic search engine implemented using MapReduce for querying RDF datasets from Egyptian chemical food laboratories. Two deployment models are analyzed: a single-machine implementation using Jena TDB and a distributed MapReduce-based system using Apache Rya on Hadoop. Performance evaluation focuses on two key metrics: uploading time and query execution speed. Experimental results show that the MapReduce-based system significantly improves both aspects, reducing upload time and query response time compared to the single-machine approach. The distributed nature of Hadoop enables better scalability and efficiency in handling large RDF datasets. A case study conducted in Egyptian food laboratories highlights the system’s real-world impact, demonstrating improved accessibility to structured data for chemists. The findings confirm that a semantic search engine with MapReduce offers a robust and scalable solution for managing food safety and nutritional data. Future work will explore machine learning-based query optimization, cloud deployment, and real-time data processing to enhance performance further.