A good in-depth analysis of query execution time with optimization in relational databases, focusing on mainly two strategies which are cost-base and index merge by using e-commerce as a case study. In this era, the data is increasing with its complexity in e-commerce which needs efficient query optimization techniques that ensure accurate data retrieval by reducing the time. We explore the main principles of cost-based optimization, which involves the cost of different query execution plans evaluation and selecting the most efficient plan. In addition, we examine the index merge technique that reduces query execution time using multiple indexes concurrently. By doing various experiments on e-commerce databases, we show the improvements in query execution time achieved by these strategies. The result focuses on the best effectiveness of these two strategies in reducing resource usage and enhancing performance of relational database management systems. Our findings help database administrators and developers who are seeking to optimize query performance in large scale database or data-intensive applications.