Databases vary greatly in size, from small datasets to collections containing terabytes of information. As databases grow, the time required to retrieve data increases, slowing server response times. While advancements in hardware can help, they often fall short of meeting the demands of large-scale data processing. Optimizing SQL queries offers a more efficient solution for improving database performance. With data volume expanding exponentially, the urgency for effective query optimization has intensified. This paper introduces a novel method for optimizing SQL query execution, focusing on performance and usability. The approach synthesizes complex SQL queries from multiple simpler ones to improve execution speed, especially when traditional methods are insufficient. A core strategy involves replacing the commonly used IN operator with a temporary table and a non-clustered index. This method significantly reduces data retrieval times by lowering the number of logical references, resulting in faster data selection, more efficient resource use, and reduced server load. The study achieved its goals through three main tasks: (1) Analyzing existing SQL optimization techniques and their limitations, (2) developing a new optimization method to enhance performance and scalability, and (3) testing the effectiveness of this method through experimentation. This paper contributes a practical solution to the growing demands of modern database systems, providing a scalable, efficient approach to handle increasing data volumes and operational requirements.