There are several significant improvements that the healthcare sector needs. There are countless opportunities to use technology to deliver more accurate, effective, and impactful treatments at precisely the appropriate time in a patient's care, from chronic illnesses and cancer to radiography and risk assessment. Artificial intelligence (AI) is set to be the engine that drives advances across the care continuum as payment mechanisms change, patients expect more from their providers, and the volume of available data continues to expand at a startling rate.The methods that people represent the same concepts or connections in healthcare literature (including EMR, claims, clinical notes, test data, pathology reports, clinical trials, and more) vary greatly. Data scientists may clean up natural language (i.e., text documents) into a structured, formal representation by using Natural Language Processing (NLP) to capture this diversity and provide a normalised representation of the text. When installing an ML model, the quest of high-quality data sets presents several difficulties. The performance of a model will be determined by the calibre of the data in feature sets. It can be difficult to keep up with the constant influx of new programming libraries and tools, and doing so takes time away from evaluating important data. Additionally time-consuming are the data's collecting and cleaning in order to achieve the necessary quality. According to estimates, the healthcare sector spends 80% of its time cleansing data and just 20% of that time finding insights.Within the pharmaceutical and healthcare industries, NLP has lately seen a rise in popularity. Deep learning algorithms' ability to analyse text input and extract information relevant to the circumstance may be primarily blamed for this. Finding patterns in medical data is one use for machine learning, and it is also quite good at predicting the disease. In this session, I will discuss on how NLP and Machine learning helps in Healthcare sector and its applications.