<p>This study introduces a novel Horned Lizard Elman Model (HLEM) aimed at optimizing the performance of Static Random Access Memory (SRAM). The Horned Lizard optimization explores the design space to identify optimal parameter configurations that enhance performance metrics such as speed, power consumption, and space utilization. The adaptive properties of the optimization are leveraged to improve the factors of SRAM, including cell size, access restrictions, and cache settings, to handle large data. Additionally, it includes a data compression process to optimize memory utilization, minimize power consumption, and reduce memory access time. This approach not only enhances SRAM efficiency but also addresses increasing demands for more capacity and faster processing in the modern computing environment. Hence, it is implemented in the Python system and the effectiveness of the proposed HLEM is evaluated by analyzing significant metrics such as the computation time, the processing speed, power consumption, capacity, and the memory Usage. These performance metrics are compared with other conventional models to validate the necessity and advantages of the optimized design.</p>