Lymphedema is a chronic and progressive condition characterized by abnormal accumulation of lymphatic fluid, frequently resulting from oncological interventions such as lymph node removal or radiotherapy. Traditional diagnostic techniques, though effective, often involve invasive procedures or expensive imaging systems, limiting their use for early screening and continuous monitoring. This thesis presents a novel, non invasive diagnostic approach combining microwave sensor data with machine learning techniques to detect, localize, and assess the severity of lymphedema. Using a MiniVNA Tiny vector network analyzer connected to a split ring resonator sensor, frequency dependent return loss measurements were collected from both healthy individuals and lymphedema patients.