Background: Prostate cancer is a pressing concern, demanding early and accurate detection for effective intervention. Conventional methods, like digital rectal exams and prostate-specific antigen (PSA) tests, often lack the required specificity. Circulating microRNAs (miRNAs) have emerged as promising non-invasive biomarkers for various cancers, including prostate cancer. The i-Biomarker CaDx is a cutting-edge, technology-neutral platform designed for multi-cancer early detection, boasting an impressive accuracy rate of 99-100%. This study aims to assess the effectiveness of i-Biomarker CaDx specifically for prostate cancer using publicly endorsed datasets while leveraging explainable artificial intelligence (XAI) to gain deeper insights. Methods: The utilized dataset (GSE112264) contains 809 prostate cancer patients matched with an equal number of age-matched healthy controls from multiple datasets. Microarray technology was employed to profile circulating miRNAs. A diverse range of classification paradigms, including decision trees, neural networks, and support vector machines, were utilized for attribute identification, classification, and seamless integration of XAI. Rigorous hyperparameter optimization was conducted, and the best-performing classifiers were amalgamated into the final model, weighted by their respective efficacies. We assessed i-Biomarker CaDx's performance through rigorous cross-validation and independent test datasets. The XAI component provided detailed insights into miRNA variations associated with diagnostic outcomes. Results: Our findings reveal that i-Biomarker CaDx exhibits exceptional diagnostic accuracy, achieving a remarkable 100% accuracy rate when evaluated on the designated dataset. Diagnostic metrics, including sensitivity, specificity, and predictive values, surpass conventional prostate cancer diagnostic methods like digital rectal exams and PSA tests. The designed models, incorporating decision trees, neural networks, and support vector machines, effectively decipher intricate miRNA relationships relevant to prostate cancer using XAI. Thus, our analyses shed light on miRNA patterns and their associations with prostate cancer, enhancing the understanding of the underlying molecular complexities of the disease. Conclusions: i-Biomarker CaDx, a pioneering technology-agnostic multi-cancer early diagnostic platform, demonstrates outstanding performance in detecting prostate cancer, surpassing traditional diagnostic methods. The integration of XAI allows for in-depth exploration of miRNA alterations and their impact on prostate cancer, enriching our interpretations at both the population and individual levels. These findings underscore the substantial potential of i-Biomarker CaDx as a transformative, non-invasive diagnostic tool for prostate cancer.