This study introduces an advanced approach to the automated detection of skin tanning, leveraging image processing and computer vision techniques to accurately assess tanning levels. A method was proposed in which skin tone variations were analyzed by comparing a reference image with a current image of the same subject. This approach establishes a reliable framework for estimating tanning levels through a sequence of image preprocessing, skin segmentation, dominant color extraction, and tanning assessment. The hue-saturation-value (HSV) color space was employed to quantify these variations, with particular emphasis placed on the saturation component, which is identified as a critical factor for tanning detection. This novel focus on the saturation component offers a robust and objective alternative to traditional visual assessment methods. Additionally, the potential integration of machine learning techniques to enhance skin segmentation and improve image analysis accuracy was explored. The proposed framework was positioned within an Internet of Things (IoT) ecosystem for real-time monitoring of sun safety, providing a practical application for both individual and public health contexts. Experimental results demonstrate the efficacy of the proposed method in distinguishing various tanning levels, thereby offering significant advancements in the fields of cosmetic dermatology, public health, and preventive medicine. These findings suggest that the integration of image processing, computer vision, and machine learning can provide a powerful tool for the automated assessment of skin tanning, with broad implications for real-time health monitoring and the prevention of overexposure to ultraviolet (UV) radiation.