Recent advancements in the widespread use of the internet and computers have led to the storage of vast amounts of data, increasing computational power, and enhanced computer capabilities, fundamentally transforming human life. The large data sets generated through the internet provide an unlimited source of data for Artificial Intelligence (AI). These developments, coupled with current AI techniques, challenge the traditional concepts of photography and images, particularly due to the ability to produce realistic visuals. As synthetic images produced by AI become increasingly indistinguishable from real, human-made photographs, the distinction between the two is becoming more difficult over time. Considering the development of Large Language Models (LLM), which can produce highly fluent and coherent communication almost indistinguishable from human-generated text within a few years of their introduction, it is expected that synthetic images will rapidly increase in realism as well. This situation has become a critical issue in preventing disinformation and maintaining visual credibility. This study addresses this problem and aims to present a holistic approach for distinguishing between real and synthetic images. In this context, a comprehensive evaluation framework consisting of six main criteria and eighteen sub-criteria is presented, and the relative importance of these criteria is analyzed using the Spherical Fuzzy AHP (SF-AHP) method. These importance levels should be continuously updated and discussed as technology evolves. In this study, an innovative approach is used, incorporating expert opinions (such as those from advanced language models like ChatGPT o-3), and based on SF-AHP analysis, physical consistency and sensor trace analysis emerged as the most critical determinants for distinguishing between synthetic and real images. The findings emphasize the necessity of a multi-criteria approach in AI image detection and provide insights for future validation methods.