Cellulite is a common condition affecting the skin and tissues, mainly experienced by post-pubertal females in developed countries. Infrared (IR) thermography combined with artificial intelligence (AI) can detect different stages of cellulite, offering a reliable diagnosis. The goal of this research was to create a fast and cost-effective method to automatically identify cellulite stages using IR imaging for prescreening and personalized therapy. The study used advanced algorithms and classification techniques on images from 212 female volunteers aged between 19 and 22. The results showed that the combination of certain methods achieved an accuracy of over 80% in determining all stages of cellulite, with more than 90% accuracy for early stages. This computer-aided approach could be used for early diagnosis, monitoring progress, and assessing therapeutic outcomes objectively. IR thermography with AI has the potential to become an effective tool in understanding cellulite development and tailoring treatments, contributing to predictive, preventive, and personalized medicine (PPPM).