Worldwide, second deadly cancer encompassed by males is Prostate cancer. With the outcome of artificial intelligence, diagnosis along with analysis of prostate cancer has become more accurate. Artificial intelligence methods can rapidly examine big amount of data which includes images and tissue specimens, to recognize sequence and based on sequence prognosis can be performed related to prostate cancer. This paper includes a dataset containing 100 patient observations from prostate biopsies. Data pre-processing is performed to clean the data, including label encoding and Min–Max scaling. Three machine learning models, Support Vector Machine, Random Forest, and Logistic Regression are evaluated. Logistic regression achieved the highest accuracy of 0.86667 when compared to the other two machine-learning algorithms. The study’s findings can benefit medical practitioners and researchers for the enhancement of prostate cancer treatment.