With the expanded prominence of online informal community, spammers discover these stages effectively available to trap clients in noxious exercises by posting spam messages. In this work, we have taken the Twitter stage and performed spam tweets identification. To stop spammers, Google Safe Perusing and Twitter's BotMaker instruments identify and square spam tweets. These instruments can square noxious connections, anyway, they can't ensure the client continuously as ahead of schedule as could be expected under the circumstances. Along these lines, businesses and specialists have connected diverse ways to deal with make spam free informal community stage. Some of them are just founded on client-based highlights while others depend on tweet based highlights as it were. Nonetheless, there is no extensive arrangement that can solidify tweet's content data alongside the client based highlights. To illuminate this issue, we proposed a system which takes the client and tweet based highlights alongside the tweet content component to order the tweets. The advantage of utilizing tweet content element is that we can recognize the spam tweets regardless of whether the spammer makes another record which was unrealistic just with client and tweet based highlights. We have evaluated our solution with two different machine learning algorithms namely – Support Vector Machine and Random Forest. We are able to achieve an accuracy of 86.75% and surpassed the existing solution by approximately 17%.