Prostate cancer is the most frequent malignancy and the leading cause of cancer-related mortality globally. A precise survival estimate is required for the effectiveness of treatment to minimize mortality rate. A remedial strategy can be planned under the anticipated survival state. Machine Learning (ML) methods have recently garnered considerable interest, particularly in developing data-driven prediction models. Unfortunately prostate cancer has received less attention to such studies. In this paper, we have built models using machine learning methods to predict whether a patient with prostate cancer would live for five years or not. Compared to prior studies, correlation analysis, a substantial quantity of data, and a unique track with hyperparameter adjustment boost the performance of our model. The SEER(Surveillance, Epidemiology, and End Results) database provided the data for developing these models. The SEER program gathers and disseminates cancer data to mitigate the disease’s effect. We analyzed prostate cancer patients’ five-year survival state using about seven prediction models. Gradient Boosting, Light Gradient Boosting Machine, and Ada Boost algorithms are identified as top-performed prediction models. Among them, a tuned prediction model using the Gradient Boosting algorithm outperforms others, with an accuracy of 88.45%  and found fastest among the other models.