Recent advances in large language models (LLMs) have unlocked remarkable long-context capabilities, enabling breakthroughs across diverse NLP tasks. However, despite architectural progress, the key-value (KV) cache remains a critical memory bottleneck during inference. Prior work has explored cache optimization via eviction strategies, yet most rely on heuristic or single-axis importance metrics, neglecting the nuanced and dynamic interplay between layers and attention heads. In this paper, we propose SCORE (Similarity-aware Contextual Overlap-Redundancy Eviction), a novel framework that introduces a distance-based multi-level similarity metric to quantify and eliminate structural redundancy within the KV cache. By dynamically reallocating cache budgets across layers and heads and employing a redundancy-aware greedy token selection mechanism, SCORE preserves semantic diversity while minimizing memory overhead. Extensive experiments on long-context benchmarks such as LongBench and NeedleBench show that SCORE retains 96.2% of full KV cache performance using only 1.5% of the cache, consistently outperforming state-of-the-art baselines under strict memory constraints. These results underscore the value of fine-grained, context-aware cache management for scalable and efficient long-context inference in LLMs.