Systematic Literature Review (SLR) is an integral part of research; however, to conduct an SLR, the manual review process would be time-consuming due to the high volume of literature involved. This paper presents a method that uses Natural Language Processing (NLP) and Machine Learning (ML) techniques to automate the systematic review process at two key stages: screening and eligibility assessment. The approach utilizes BERT (Bidirectional Encoder Representations from Transformers) embeddings and cosine similarity for automated label assignment and categorization of research articles. The proposed approach aligns to the principles of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) in the automation of the domain based categorization of articles, and resolves the challenges posed by the state of art techniques. The proposed work considers three datasets with total 2164 records, collected based on identification criteria of PRISMA, to be classified into three categories. The proposed method based on BERT gives good results on categorization of article with an accuracy of 99% , 98% and 96% for each dataset.