This thesis presents a novel approach to renal failure detection, proposing the use of voice analysis as a non-invasive biomarker. Renal failure, also known as kidney failure, is a condition affecting around 10% of the global adult population, occurs when the kidneys are unable to efficiently filter waste from the bloodstream, leading to fluid and toxic accumulation and other severe health complications. Current diagnostic methods rely on clinical assessments and laboratory tests, which are often time-consuming, resource intensive and stressful for the patient. This research explores an alternative, automated detection method by focusing on changes in vocal characteristics, hypothesizing that fluid retention can influence the voice in measurable ways.