The Short message service (SMS) has been promptly developing these days for businesses, marketing, advertisement, and information. Because of its popularity and easy access people use it for fraud as well. A short message device is the most common and popular communication medium through which text sends electronically. Spams are undesired or unfavorable message in form SMS that is passed on the communication medium. Spam causes many problems like limited memory space and also can affect computing power and speed. Nowadays, spam messages have been overflowing in many countries. Spam is the most annoying thing for the individual. The main issue with spam messages violates privacy. This study presents a literature review of the machine and deep learning techniques used in the detection, classification, and spam filtering for SMS spam. In this review, different databases were used for search including research gate, ELSEVIER, Applied sciences, and IEEE. SMS is a more commonly used media than email. This study gives you an overview of the machine and deep learning methods, graphical representation method, and automatic spam filtering methods from android previously used for SMS spam detection and filtering. The main objective is to find the limitations of the previous studies and suggestions for future work. Keywords: Machine Learning, Support Vector Machine, Deep Learning, Graphical representation, Conventional Neural Network.
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