Traditional tokenization methods in language models often encounter challenges in efficiently capturing semantic complexities and managing computational resources. The introduction of Hierarchical Memory-Based Adaptive Tokenization (HMAT) addresses these limitations through a dynamic, contextaware approach that constructs a multi-level semantic hierarchy, enabling models to process information at varying degrees of abstraction. Empirical evaluations demonstrate that HMAT enhances tokenization efficiency, reduces computational overhead, and improves semantic coherence in generated outputs. These findings demonstrate the potential of HMAT to advance tokenization strategies within language models, offering a pathway toward more sophisticated and adaptable natural language processing systems.