Prostate Cancer (PCa) is one of the most frequent cancers worldwide and the most common cancer in males. Testing for PCa remains problematic. Evidence is mounting that overdiagnosis and over-treatment can result in adverse side-effects yet have little impact in preventing death from PCa. Consequently, the importance of predictive tools that help physicians in the diagnosis of the condition cannot be understated. Though there exist several predictive models for the detection of clinically significant PCa, these models mainly depend on logistic regression. The objective of this research is to investigate the potential of various machine learning techniques to improve the sensitivity and specificity of detecting clinically significant PCa. Risk factors considered include prostate-specific antigen (PSA), digital rectal examination (DRE), as well as age, race/ethnicity, and family history. According to the results, Logistic Regression has outperformed all the models followed by Random Forest, SVM and XG Boost.