Varicose veins are enlarged, twisted veins that commonly appear on the legs and are caused by weakened or damaged vein walls and valves. Factors like aging, genetics, pregnancy, obesity, and prolonged standing or sitting can increase the risk of developing varicose veins. It leads to abnormal blood pooling and increased skin temperature around affected areas. This study offers an automated diagnostic system for identifying and categorizing varicose veins utilizing MATLAB image processing algorithms and infrared thermography (IR). The suggested method classifies varicose veins into five phases according to intensity and affected area, detects afflicted areas, and analyzes thermal patterns in infrared images. Using predetermined threshold criteria, a region-of-interest (ROI) mask is used to classify the severity of varicose veins. An overall evaluation based on the highest identified stage is included in the tabular presentation of the analysis results, which are graphically represented by bounding boxes of various colors that correlate to severity levels. This automated system offers a non-invasive, economical substitute for conventional techniques while improving the precision and effectiveness of varicose vein diagnostics.