A state-of-the-art method in machine learning is called Multimodal Generative Artificial Intelligence (AI), which aims to produce a variety of outputs in many modalities, including text, audio, and images. This survey explores multimodal generative AI’s developments, techniques, and uses. First, we clarify the main concepts behind such models, such as adaptive autoencoders (VAEs), generative adversarial networks (GANs), and their various extensions. The methods for fusing and aligning different modalities—such as conditional generation, cross-modal embeddings, and attention mechanisms—are then covered. A cutting-edge machine learning method called multi-modal generative artificial intelligence (AI) generates multiple methods of outputs such as text, audio, and visuals. This survey examines the progress, methods, and applications of generative AI. The article also examines various applications of multimodal generative AI, including image captioning, music composition, and text-to-picture synthesis. We also discussed challenges and ethical considerations related to multimodal content, such as privacy issues and prejudice reduction. Finally, we focus on a research roadmap that emphasizes the need for a multimodal, socially responsible, controlled, and comprehensible AI system.