Generative Adversarial Networks (GANs) [1] have revolutionized image synthesis by producing highly realistic images; however, evaluating the quality of these outputs remains a significant challenge. This paper provides a critical analysis of the current evaluation metrics used in GAN research, discussing both their strengths and inherent limitations. We review widely adopted quantitative measures such as the Fréchet Inception Distance (FID) [2] and Inception Score (IS) [3], which, despite their popularity, often fail to fully capture perceptual quality and diversity due to sensitivity to sample size, data distribution, and implementation nuances. Additionally, we explore alternative metrics - including Precision & Recall [4], and Learned Perceptual Image Patch Similarity (LPIPS) [5] - highlighting how these approaches aim to reconcile objective measurements with subjective human perception. By categorizing evaluation methods into quantitative versus qualitative, pixel-based versus concept-based, and reference-based versus no-reference, our study presents a comprehensive taxonomy that clarifies the trade-offs involved. Finally, we outline future perspectives, suggesting the integration of hybrid and probabilistic methods to develop more robust, interpretable, and holistic evaluation frameworks for generative models.