Nowadays, Twitter is identified as one of the largest social networking sites and has become a huge part of many people’s lives. Many activities, such as communication, promotion, adver- tisement, news, and agenda creation, have begun to be carried out through it. Twitter offers a primary feature called trending hashtags, where a name, phrase, or topic that is preceded with a hash sign (#) is mentioned at a greater rate than others. Twitter’s trending hashtags attract much attention; thus, they can affect the public agenda. With over 230 million users on Twitter, it comes as no surprise that this valuable feature has been abused by malicious campaigns. In the wrong hands, Twitter’s trends can be used to mislead people and disseminate fake news. One way to manipulate trending hashtags is through hashtag hijacking, where a trendy hashtag on social media is taken over to promote a different message than the originally intended one. Hashtag hijacking is normally done by fake accounts, especially by spammers or trolls. This type of hashtag manipulation can have several negative impacts on the original message or the reason for being promoted. It can confuse users, blur the original message, and lead to online harassment and trolling, in addition to damaging reputations. In this project, eleven pioneer- ing machine learning algorithms are used separately to build a detection system for trending hashtags, and their results are compared to find the best-performing one. Experimental results revealed that the best-performing model is SVM trained with TF-IDF using oversampling which achieved an accuracy of 0.980. However, the VotingClassifier model trained with TF-IDF has a slightly higher accuracy of 0.981, but it incurs an overhead of 23 minutes to run compared to SVM which only tooks seconds.