Matrix multiplication plays a pivotal role in various domains, including image processing and artificial intelligence, making its optimization a crucial endeavor. This research paper delves into enhancing the efficiency of matrix multiplication by specifically focusing on reducing the number of additions required in the process. Traditional methods typically necessitate between 15 to 18 additions; this study aims to lower this count, thereby streamlining computational requirements. The methodology encompasses two primary stages: Through this multi-faceted approach, the paper aims to contribute to the field, potentially leading to more efficient algorithms in image processing and AI applications.