HBRP Publication Page 1-7 2021. All Rights Reserved Page 1 Research and Applications: Embedded System Volume 4 Issue 3 e-ISSN: 2582-2993 Review on Efficient Spam Detection Technique using Machine Learning Tejal S. Murkute* 1 , .Nitin K. Choudhari 2 , Dipalee M. Kate 3 1 PG Student, Department of Electronics (Communication) Engineering, Priyadarshini Bhagwati College of Engineering, Nagpur, Maharashtra, India 2 Professor, Department of Electronics (Communication) Engineering, Priyadarshini Bhagwati College of Engineering, Nagpur, Maharashtra, India 3 Assistant Professor, Department of Electronics(Communication) Engineering, Priyadarshini Bhagwati College of Engineering, Nagpur, Maharashtra, India *Corresponding Author E-Mail Id: tejalmurkute19@gmail.com ABSTRACT People's communication methods are being transformed by electronic mail because of its affordability, speed, and simplicity. Due to their widespread exposure, spam emails have become a serious roadblock in electronic communication. The amount of time users sifting through incoming mail and eliminating spam necessitates the implementation of spam detection software. The main objective is to create suitable filters that can correctly recognise these emails and deliver outstanding performance in the majority of cases. This project makes use of Spam Detection to tell spam from valid email. SVM, a machine learning method, is employed in this case to assess it. SVMs and other approaches of machine learning (AI) Spam detection can benefit greatly from machine (SVM) detection. This project's classification is based on its features. In the email world, spam is a term that refers to unsolicited commercial communications or emails that deceive the recipient. With the use of artificial intelligence and machine learning, spam messages can be identified. Spam filtering is a popular application of machine learning techniques. Machine learning classifiers are used to identify emails as either ham (legitimate messages) or spam (unwanted messages) using these techniques. Keywords: Email spam detection, spam detection, machine learning INTRODUCTION The Internet has become a significant communication tool due to its widespread use and inexpensive cost. Spam has increased dramatically with the Internet and email. Spam can originate from anyone on Earth with a computer. These technologies can be abused to deliver unwanted mass communications or advertise things and services that are generally despised. Spamming using these systems is a crime. Building spam filters that can adequately delete the rising numbers of junk emails before they reach user mailboxes is extremely difficult. Daily E-mail identification will negatively affect worker productivity and mental wellbeing.[1-3] The importance and utility of spam detection cannot be emphasized. "Spam" refers to spammers altering and poisoning reviews for profit. Review spam detection is critical since not all internet reviews are reliable. By extracting significant aspects from the text and applying machine learning techniques, NLP can identify review spam. Aside from the content itself, reviewer information might help in this process. In this study, we examine some of the most prominent machine learning algorithms for identifying review