Hash functions are pivotal in numerous cryptographic protocols and security applications, including message authentication, data integrity verification, and password storage. Despite the widespread use, many hash functions exhibit vulnerabilities to collision attacks and suffer from time inefficiency. Therefore, efficiently utilizing time, RAM, and CPU resources is crucial in evaluating hash functions for performance optimization, especially in real-time processing or large data volumes. Firstly, efficient utilization of these resources is essential for performance optimization, especially in scenarios involving real-time processing or large data volumes. Additionally, scalability is vital as hash functions must effectively handle increasing workloads without causing bottlenecks. Moreover, minimizing resource consumption in resource-constrained environments such as embedded systems or mobile devices is critical to prevent system slowdown. Lastly, inefficient hash functions may indirectly impact security by reducing system responsiveness or susceptibility to denial-of-service attacks. Thus, optimum time, RAM, and CPU consumption enable the selection of hash functions that balance performance, scalability, resource constraints, and security requirements across diverse applications and environments. Therefore, this research thoroughly evaluates 20 different hash algorithms, assessing key performance metrics such as time consumption, RAM utilization, and CPU usage across applications handling multimedia files ranging from 1 KB to 1 GB. The study aims to identify robust algorithms while optimizing performance by scrutinizing these factors.