Sexually transmitted disease (STDs) is of serious concern, especially among the youths. Efforts to eradicate such diseases are often frustrated due to different sociodemographic elements and clinical factors that usually lead to misdiagnosis. Thus, this paper applied six (6) distinct machine learning models to accurately analyze STD infections reported by 400 patients that attended Federal Polytechnic Ilaro Medical Center, Ogun State Nigeria. Weak non-significant correlations were obtained between the 7 symptoms considered for the diagnoses and the diagnosis outcome, but no significant pattern was observed. However, the application of data mining tools revealed a hidden pattern that correctly predicted the outcome using the subjects’ symptoms, age, and sex. 4 out of the 6 machine learning models were adjudged to perform well using different performance metrics, of which Logistic regression model was found to be the best. The model feature importance chat shows that Vagina Discharge and Vagina itching have the highest and almost the same level of impact on the possibility of a diagnosed patient having STDs. Furthermore, a 100% performance of logistic regression implies that the model correctly predicted all the 309 true negatives and 101 true positives with a misclassification (misdiagnosis) of zero.