Reviewer assignment is the process of matching submitted articles with qualified reviewers for objective feedback. Reviewer Assignment Problem (RAP) refers to the challenge of finding qualified reviewers and matching them with publications that meet minimal requirements. The goal of this study is to gain a better understanding of the RAP domain by investigating the current state of research, the breadth and depth of existing approaches, the potential for future development, the difficulties inherent in the field, and the potential for novel solutions to the reviewer assignment problem. According to the results, researchers in the RAP area are mostly interested in finding ways to increase automation, fairness, accuracy, and subject coverage. The primary objective of the proposed study is to create a proactive mechanism for assigning reviewers to papers using machine learning methods. The study suggests two systems that are built utilising machine learning techniques. Unsupervised learning based Proactive Reviewer Paper Assignment System (UPRPAS) and deep learning based Proactive Reviewer Paper Assignment System (DPRPAS) are the names of the two systems presented. URPAS is an unsupervised method that uses Latent Dirichlet Allocation to construct a reviewer and article topic model.