Biomedical Named Entity Recognition (Bio-NER) contributes significantly to the advancement of biomedical research by facilitating efficient information extraction, text mining, and knowledge discovery. This in-depth analysis examines the applications and significance of Bio-NER within the context of biomedical research. Following an overview of Named Entity Recognition (NER) in natural language processing, this paper discusses the distinctive characteristics and challenges of Bio-NER. This article focuses primarily on deep learning-based approaches for Bio-NER, including single neural network-based methods, multitask learning-based strategies, transfer learning in Bio-NER, and hybrid model-based techniques. Using deep learning, this paper also addresses the obstacles in Bio-NER, evaluation metrics, benchmarks, datasets, and the general framework of Bio-NER. In conclusion, this paper examines the most recent Bio-NER tools and frameworks based on deep learning. This review is a useful resource for researchers and practitioners who wish to comprehend the current state of Bio-NER using deep learning and its potential to revolutionize biomedical research and improve human health.