<p>Generative Adversarial Networks (GANs) have emerged as a formidable class of artificial intelligence frameworks, offering significant advancements in the synthesis of realistic data across different domains. The continuous evolution of GAN architectures and optimization techniques has expanded their applicability, yielding more stable and high-quality outputs. This systematic survey aims to encapsulate state-of-the-art developments in the domain of optimized GANs, providing an exhaustive overview of recent innovations, methodologies, and applications. This article provides a comprehensive review of various Generative Adversarial Network architectures, including Conditional GAN, Wasserstein GAN, Deep Convolutional Generative Adversarial Network, Cycle-consistent GAN, Progressive GAN, StackGAN, and StyleGAN. This survey examines the important roles of Multilayer Perceptron and Convolutional Long Short-Term Memory architectures in generating data, while the DenseNet121 and AlexNet models function as discriminators to differentiate between real and synthetic data. We delve into the iterative enhancements in network designs, binary cross entropy, minimax and Wasserstein loss functions, training stability, and application-specific customizations that have propelled GANs to the forefront of generative models. Furthermore, this survey addresses the critical challenges faced in GAN optimization, such as mode collapse, convergence issues, and evaluation metrics, offering insights into potential solutions and future research avenues. The comprehensive analysis of various domains such as electricity demand forecasting, supply chain inventory analysis, medical image synthesis, agriculture data augmentation, portfolio management and stock prediction operates as a foundation for researchers as well as practitioners striving to harness the power of the optimized GANs ranging from image and speech generation to complex data augmentation tasks.</p>