Suicidal detection and treatment from the clinical and public health perspective are reactive. For an action whose consequences are irreversible, a reactive approach to the problem cannot be the answer. A proactive approach is needed to solve and detect suicidal intent. Social media has become the television and diary of millennials and Gen z alike; hence, it is imperative to create techniques and approaches to study their actions in this particular space. This research involved creating document similarity algorithms from Corpora mined from the Twitter Developer API. Making the data unique to this platform, a methodology design involving validating data at various spectrum and selecting an appropriate threshold to classify the similarity levels were created as well as a lexicon unique to the Twitter Dataset. With an accuracy score of 84%, the Jaccard document similarity algorithm was able to spot suicidal intent from user’s tweets, and with an accuracy of 93%, it was also able to spot non-suicidal intent. The Jaccard model seemed to be the most durable and computationally efficient for the problem and was chosen as the algorithm for detecting suicidal tendencies in users’ tweets.