Generative Artificial Intelligence (AI) encompasses a diverse array of models designed to produce new data that closely resembles existing datasets, spanning modalities such as text, images, audio, and more. This review systematically categorizes and examines the primary generative model architectures: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, Transformer-based Models, Recurrent Neural Networks (RNNs), Energy-based Models (EBMs), and Reinforcement Learning (RL)-based generative approaches. For each model type, we discuss its foundational principles, representative architectures, and notable applications, providing insights into their respective strengths and limitations.

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