Natural Language Processing (NLP) workflows in biomedical domains face unique challenges due to specialized terminologies and the need for high precision in downstream applications. This study presents a systematic framework for preprocessing and analyzing biomedical texts, with a focus on evaluating tokenization strategies and their impact on representation learning. We have proposed a dual-phase approach: first, benchmarking various tokenizers across efficiency and domain-specific accuracy metrics; second, integrating context-aware embedding techniques to enhance semantic capture. Our experiments reveal that SciSpacy outperforms conventional tokenizers in biomedical term recognition despite computational trade-offs, while custom-trained BPE models achieve a 22% reduction in out-of-vocabulary rates compared to generic implementations. Visualization via t-SNE and co-occurrence networks demonstrates that domain-specific embeddings cluster biomedical entities (e.g., respiratory terms, medications) with 89% intra-class similarity, significantly improving interpretability. This work bridges the gap between generic NLP pipelines and biomedical text requirements, offering actionable insights for optimizing preprocessing workflows and representation models.