Generative AI Models: A Comprehensive Review

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.Generative Artificial Intelligence (AI) refers to a class of models capable of producing new data that closely mirrors existing datasets.These models span multiple modalities, including text, images, audio, and structured data, and have become central to numerous applications in fields such as natural language processing, computer vision, music generation, scientific discovery, and creative content production.Generative AI models differ in their architecture, learning strategies, and the types of output they generate.Some rely on adversarial training, others on probabilistic latent representations, sequential modeling, or iterative refinement processes.This diversity has led to the development of several prominent generative model families, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, Transformer-based Models, Recurrent Neural Networks (RNNs), Energy-based Models (EBMs), Reinforcement Learning (RL)-based generative approaches, and hybrid architectures that combine these techniques.Understanding the mechanisms, capabilities, and limitations of these models is crucial for both researchers and practitioners seeking to develop, enhance, or apply generative AI technologies.This review provides a structured examination of these model types, highlighting their underlying principles, representative architectures, and notable applications, offering a comprehensive perspective on the current landscape of generative Generative Adversarial Networks consist of two neural networks: a generator and a discriminator.The generator produces new data samples that closely resemble real data, while the discriminator evaluates whether a given sample is real or generated.Through this adversarial process, the generator gradually improves until the discriminator can no longer reliably distinguish between real and generated samples.GANs are widely used