Nowadays, E-commerce websites has become a vital part of regular life. The pandemic has taken a quick step towards the digital world and initiated changes in online shopping behaviors. While buying products online many people read the reviews related to the product and then decide on buying it. So, reviews play an important role if a customer wants to buy any product. This leads to increased spam review volume in social networks and e-commerce websites like Flipkart, Amazon, etc. Fake reviews can be used to demote a good product or to promote a bad product, so there is a need for robust and reliable techniques to detect fake reviews which can be beneficial to the customer as well as to the vendor. The objective of this survey paper is to get an overview of different methodologies used to solve such problems. This research presents a systematic review on methods to detect spam review using different Deep Learning (DL) Approaches, Machine Learning (ML) Methods, Natural Language Processing (NLP), and Sentiment Analysis.