The rapid technological change has influenced our life in various ways where internet became the primary means of communication. A great number of people can’t dispense it, and they start relying on it for different purposes. Moreover, the internet made communicating with users anytime all over the world easier and possible in very simple and inexpensive form through emails or other communications messengers. Nowadays, emails became a unique digital way of communication between people to exchange variety of emails in daily life.\However, spam is one problem internet users face every day. Spam or in another word the Unsolicited Bulk Email is the undesirable emails users often receive for different reasons. Those emails may include either commercial content or may contain harmful content which aims to victimize some users to obtain certain user’s personal data or spreading malware. cite{Dec12} cite{new1} cite{new11}.\The increasing number of spam emails internet users receive every day made such phenomenon a significant problem due to the huge volume of the required storage cite{new3}. Machine learning techniques can be a capable solution to filter those unwanted emails where there are different classification and clustering algorithms developed for such purpose. One approach is the classification of those textual spam emails based on their contentcite{new1}. \Since spam filtering is an online filtering process on the email account level, it faces several problems due to the complexity of text patterns cite{new2}. A lot of machine learning approaches have been studied for the classification of spam email problemcite{Dec12}. \A huge number of papers are published every year making it difficult for freshmen year computer science students to choose from where to start. This overview article can be an introduction for them and for other readers interested in spam filtering techniques. Whereas the goal is to present a general overview of different spam filtering techniques algorithms.\This overview paper consists of 3 sections: The first section represents the introduction, second section a brief description of popular machine learning techniques used in spam filtering based on the email content, third section discussing how accurate these techniques to achieve their goals, and the last section provides the conclusion and a summarize of future work and challenges.\


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