Improving the performance of data warehouses is essential for efficient management and analysis of large data sets. Query caching methods can be implemented to get better efficiency. Fast access to data warehouses (DWs) is essential for contemporary BI. With the advent of big data, the cache has emerged as one of the most effective tools for speeding up data access. Decision support system (DSS) data handling and utilization has been greatly enhanced by several affiliations’ use of DW. To make data retrieval from DW more efficient, various methods have been used. Query caching is one useful approach that plays a part in optimization. Based on a cache-based technique, the proposed work helps DW in two ways: First, it reduces execution times by retrieving records directly from cache memory; second, it frees up cache memory by deleting rarely used material. Our goal is to make the most often accessed data permanently stored in the cache. This goal is accomplished by considering both the size and frequency of the data in the maturing-based least frequently used (LFU) calculation. The amount and frequency of data can be used to regulate the requirement for the expiration age of cache memory. Data saved in cache memory is age-counted by LFU, which also sets priorities. If you want to clear out the cache, you should start with the most recent and least necessary entry. Finally, by utilizing cache memory, the proposed caching approach sealed a crucial performance gap between the business user question and the vital DW.