In 2003, et al. Jerome R. Bellegarda, demonstrated conventional mail filtering techniques based on unsupervised learning where classification is done based on keyword matching. But if spammers change the tricks of framing spam messages, the old classifiers will not be able to provide accurate results. This is the worst part of unsupervised learning. On the other hand, in the same paper, machine learning techniques based on supervised learning are introduced where the classifiers are regularly fed the changing patterns of spam emails with different datasets[15]. In 2006, et al. Giorgio Fumera,focused in his work in [20] on machine learning-based text,categorization techniques and pattern recognition approaches for,analyzing the semantic content of emails instead of manually,coded rules derived from the Spam email analysis. This article has illuminated the concept of content-based spam filtering and spam filtering that takes advantage of text information embedded in images sent as attachments. In 2009, et al. Ronald Bhuleskar has tricked out a new approach of the HSF model. This is a combinatorial filter model of various spam filtering techniques. The author used unsupervised and supervised techniques simultaneously in his model. Filter your inbox through various filters separately, but all filters should be arranged in parallel. The parallel filters used in this paper were the Black and White List, the Content-Based Filter, and the Forging Filter. In spoofing, the sender's IP address is checked and then at the server level validates the domain name of the sending email server with its IP address or reverse DNS lookup[18]. In 2010, et al. Morteza Zi Hayat showed in [19], once again supervised learning is used and promoted. In this part of the article......s and Networks, IEEE Computer Society, 2009, pp. 302-307.[19] Morteza Zi Hayat, Javad Basiri, Leila Seyedhossein, Azadeh Shakery, "Content-Based concept drift Detection for email spam filtering", 5th International Symposium on Telecommunications (IST'2010), 2010, pp. 531-536.[20] Giorgio Fumera, Ignazio Pillai, Fabio Roli, “Spam filtering based on the analysis of text information embedded in images,” in Journal of Machine Learning Research, vol. 7, December 2006, pp. 2699-2720.[21] Zhenyu Zhong, Kang Li, "Accelerating statistical spam filtering by approximation," ieee transactions on computers, vol. 60, no. 1, January 2011, pp. 120-133.[22] Basheer Al-Duwairi, Ismail Khater, Omar Al-Jarrah, "Image Spam Detection Using Image Texture Features", International Journal for Information Security Research (IJISR), Volume 2, Issues 3/4, September/December 2012, pp. 344-353.