Fashion is the canvas of our identity. Fashion can be so inclusive, expressive, and sustainable. In the growing landscape of fashion, every individual faces an overwhelming array of choices. It becomes difficult to discover a personalized wardrobe that reflects their preferences, taste, and needs. Traditional Fashion Recommendation Systems (FRSs) limit their ability to scale and adapt to the ever-growing styles as they heavily rely on manual design. Around the world, a large number of users buy clothes online through e-commerce websites. These websites primarily use recommender systems. Appropriate recommendations given by FRS help to enhance user satisfaction and make it more enjoyable and accessible. Artificial Intelligence (AI) tools have revolutionized FRS, enabling it to consume beyond conventional methods by taking in contextual data, user preferences, and visual content for recommendations with a more individualized suggestion. Recently, Generative Adversarial Networks (GANs) have emerged as a potent technique to enhance these systems by generating diverse fashion designs with high fidelity. In this paper, a systematic review of parameters used to evaluate FRS using Generative Algorithms is discussed. Various parameters to evaluate system performance and the recommendation quality are analyzed. Detailed analysis of the input parameters, to be considered to design the efficient AI-based FRS (AI-FRS), is also presented. Along with this, research gaps are explored by surveying numerous review papers. This review will help in deciding the evaluation parameters to develop and examine a more efficient AI-based FRS.