In recent times, significant progress in the field of healthcare has resulted in a greater understanding of the causes of diseases, the introduction of novel diagnostic methods, and the creation of successful medical treatments. Nonetheless, obstacles remain, especially in the realm of diseases such as cancer. This cross-disciplinary investigation combines medical knowledge with sophisticated Machine Learning (ML) techniques to improve the identification of prostate cancer. By analyzing a thoughtfully compiled dataset including detailed morphological records from 100 subjects and applying techniques such as SMOTE for data balancing, this study aims to enhance predictive accuracy. It emphasizes the opportunity for integrating conventional diagnostic practices with modern ML advancements, leading to enhanced accuracy in disease identification and prevention efforts. Significantly, the study delves into the impact of different classification methods on the identification of prostate cancer, underscoring the significance of dataset magnitude and algorithm choice. The random forest classifier demonstrates the highest mean accuracy of 95%, with the decision tree classifier trailing at 92.23%. The K-NN classifier achieves a precision level of 90.06%, while the support vector classifier attains an accuracy of 87.26%. Lastly, the naive Bayes classifier registers an accuracy rate of 84.47%. Moreover, the integration of eXplainable Artificial Intelligence, which encompasses methods like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), enhances the interpretability of models, resulting in increased confidence in the results. This investigation provides crucial insights into selecting algorithms and assessing their performance in detecting prostate cancer.