Biomedical Entity Linking (BEL) aims to map biomedical mentions, such as diseases and drugs, to standard entities in a given knowledge base. BEL within short text is viewed as a ranking challenge. Current research on this topic is mostly about long text relaying on the rich contextual information, which might not suitably apply to shorter texts. Furthermore, supervised approaches face hurdles in training data suitability, particularly in domains like biomedicine characterized by entities with diverse naming conventions, including abbreviations, acronyms, synonyms with different morphological variations and word orderings. To address these challenges, we propose a BiLSTM-Stacked Layers and Attention mechanism (BiLSTM-SLA) which refers a novel approach that integrates a BiLSTM with stacked layers and attention mechanism to enhance BEL within short texts. BiLSTM-SLA aims to provide a deeper understanding of the input text by considering bidirectional context analysis and leveraging stacked layers for nuanced temporal dependencies within the candidate sequence. Moreover, the attention mechanism enables the model to dynamically focus on and assign weights to different important part of the text. BiLSTM-SLA is assessed against two types of gold standards: KORE50 and Webscope for general English short texts, and the NCBI Disease Corpus, TAC2017ADR and ShARe/CLEF datasets for the biomedical domain. The experimental findings demonstrate that BiLSTM-SLA attains state-of-the-art results across all datasets, showcasing significant superiority over baseline methods. Notably, it achieves accuracies of 87.63%, 92.87%, 91.23%, 91.74%, and 92.78% on these benchmark datasets, respectively.