The diagnosis of prostate cancer has traditionally been based on methods like digital rectal examination (DRE) and prostate-specific antigen (PSA) testing. However, these methods have limitations in specificity and sensitivity, leading to unnecessary biopsies and overtreatment. This systematic review aims to evaluate the effectiveness of urinary biomarkers and artificial intelligence (AI) in improving prostate cancer screening and diagnostics. The review looked at 349 studies and found that biomarkers in the urine, like fatty acids and microRNAs in extracellular vesicles, are much more specific and sensitive than traditional PSA tests. AI algorithms applied to medical imaging, particularly multiparametric MRI and genomic data analysis, improve the detection of clinically significant prostate cancer, reducing false positives and unnecessary biopsies. International collaborations between researchers in the United States, China, and the Netherlands have been pivotal in advancing the integration of these technologies in medical diagnostics. The findings suggest that integrating urinary biomarkers and AI systems into clinical practice could revolutionize prostate cancer diagnostics by making tests more accurate, less invasive, and tailored to individual patient needs. However, further validation in larger cohorts is necessary to ensure the clinical efficacy of these technologies and their widespread adoption.