Generative AI has evolved rapidly and demonstrated accuracy in creating content with diverse yet too realistic styles. This paper provides a complete overview of the field, starting with its core principles and continuing with some recent results and potential future applications. This also covers requirements for new task-specific and data models, including our critical generative model generation (GANs, VAE, and more) in four image audio text videos. The paper emphasizes that generative AI has the potential to transform industries and lists some of these possible applications. It also reviews GAN technology limitations, including data bias, ethical questions, and model interpretability. To maximize the potential of this technology, we stress that it is crucial to construct reliable, interpretable, and self-sustainable generative models. The paper ends with a discussion of future directions, new trends in multimodal models, and scopes of energy-efficient approaches. Knowing the risks and potential ahead is how researchers and practitioners can make an informed choice to use generative AI for better or worse.