Biomarkers have emerged as powerful classification features in the development of neural network-based, machine learning, and AI-driven prognostic models within the field of personalized medicine. Their ability to provide quantifiable insights into a patient’s health state makes them invaluable for targeted interventions, risk assessment, and precision treatment planning. Biomarkers are measurable indicators derived from blood samples, tissue analysis, or other bodily fluids, offering objective metrics that aid in medical decision-making. For example, an electrocardiogram (ECG), which records electrical signals from the heart, serves as a biomarker for cardiovascular health, enabling clinicians to evaluate heart function and detect abnormalities. By leveraging such indicators, biomarkers contribute to actionable insights, making them essential tools in patient management, disease monitoring, and treatment administration. This chapter introduces a medical application built upon a machine learning backbone that utilizes biomarkers retrieved from blood tests to define various health states, including obesity, metabolic syndrome, and systolic blood pressure anomalies. The system is designed and implemented using the Rational Unified Process (R.U.P.) and the Cross-Industry Standard Process for Data Mining (CRISP-DM), ensuring a structured and iterative development approach. By adopting and adapting industry standards, this work aims to address sector-specific requirements and challenges while designing intelligent, AI-driven solutions that add value to all healthcare stakeholders. The integration of new technologies in medicine holds the potential to redefine healthcare pathways, bridging the gap between laboratory research and real-world patient applications. However, the clinical adoption of AI-driven healthcare solutions is contingent upon strong medical validation and rigorous regulatory oversight to ensure usability, reliability, and patient safety. In the development of healthcare applications, compliance with regulatory frameworks is a key challenge, requiring continuous validation and oversight throughout the software lifecycle. A fundamental aspect of this work involves defining the conditions under which a healthcare software application qualifies as a medical device, as this distinction directly impacts development constraints, legal requirements, and compliance measures. Given the critical role of regulatory bodies in shaping healthcare technology, this chapter also explores strategies for effectively managing regulatory interactions throughout the production cycle. By incorporating validation protocols, risk assessment models, and transparent AI methodologies, the proposed system seeks to align technological advancements with medical standards, ensuring that AI-powered applications are clinically viable, ethically sound, and legally compliant.