People's perceptions are influenced by several sentences. The reader is better able to understand the statement through various entities thanks to these perceptions. Named Entity Recognition (NER) is the term used for this technique in NLP. Confusion about whether a word represents the name of a person, place, or organization, or whether an integer represents a date, time, or amount of money, is one of the main issues in these NLP processes. One of the difficult jobs that previously required a great deal of expertise in the area of feature engineering along with lexical databases to attain good performance is named entity recognition. Because of this, we create Scikit-learn as well as Keras algorithms for NER algorithms to accurately label the entities and divide them into the most likely and unlikely transitions. Finally, we implement Biological NER Labelling on protein and gene sequences to conclude our work.