This paper presents a comprehensive comparison of distributed caching algorithms employed in modern distributed systems. We evaluate various caching strategies including Least Recently Used (LRU), Least Frequently Used (LFU), Adaptive Replacement Cache (ARC), and Time-Aware Least Recently Used (TLRU) against metrics such as hit ratio, latency reduction, memory overhead, and scalability. Our analysis reveals that while traditional algorithms like LRU remain prevalent, hybrid approaches incorporating machine learning techniques demonstrate superior performance in dynamic environments. Additionally, we analyze implementation patterns across different distributed architectures and provide recommendations for algorithm selection based on specific workload characteristics.