The worldwide concern about prostate cancer continues to grow, while the necessity for fast and precise diagnostic tests that are easy for patients to use becomes increasingly urgent. This section examines recent developments in how biosensors, combined with artificial intelligence (AI) and machine learning systems, enhance our ability to identify and monitor prostate cancer. Modern biosensor applications, which incorporate electrochemical and optical detection systems alongside nanoscale and wearable devices, now enable the detection of biological markers specifically for prostate cancer indicators, such as prostate-specific antigen, prostate cancer antigen 3, and transmembrane protease, serine 2: ETS-related gene fusion. The analytical procedure for processing and understanding biosensor-generated complex information involves signal filtering steps, along with feature extraction and normalization, before addressing missing value management. The analysis covers AI processing methods, starting with supervised algorithms, and continuing to deep learning solutions, including convolutional neural networks and recurrent neural networks, which interpret biosensor readings to generate immediate, actionable information. Medical diagnosis systems developed with sensor information and clinical and genetic data produce personalized systems that achieve better accuracy levels. These days, wearable diagnostic systems that offer continuous alarm detection work in tandem with Edge AI algorithms that operate directly on devices. We evaluate both privacy concerns related to data and review the reliability of sensors, as well as the technical aspects of clinical system integration. The text examines current and future health developments by discussing digital twins, quantum computing, and virtual healthcare systems. The chapter presents an innovative assessment of future possibilities for AI-equipped biosensors to enhance prostate cancer diagnosis through improved efficiency, individualization, and increased accessibility.