Growth of electronic health record (EHR) systems in health care has immensely improved the retrieval and handling of patient data. Even after these advancements, a huge amount of important clinical information is stored in unstructured formats such as clinical notes, discharge transcripts, and medical histories. Extracting some useful insights from such unstructured data is difficult due to the complexity of medical language, the frequent use of abbreviations, and inconsistent document structures. This paper reviews the research work that has been done in the field of natural language processing (NLP) and machine learning (ML) techniques to overcome these challenges. The main focus of this paper is to convert unstructured biomedical data into structured data. We also discuss approaches for removing ambiguity of clinical abbreviations to provide their context-based full forms. Deep learning models have shown more accurate performance in processing clinical texts, enabling better data extraction and management. Our proposed solution involves developing a NLP-based framework to follow a universally accepted standard format for both handwritten prescriptions and structured biomedical data, improving the quality and usability of EHR data for patient care and biomedical research. The paper outlines the key techniques used for data structuring and abbreviation expansion and discusses the future directions in biomedical NLP.