A Comprehensive Survey of Generative AI: Applications and Future Directions Across Domains

Abstract

Generative Artificial Intelligence (GenAI) has become a foundational paradigm for learning complex data distributions and synthesizing realistic content across text, image, audio, tabular, scientific, and multimodal data. Despite rapid progress, the literature remains fragmented across model families and application domains, making it difficult to compare methodological choices, evaluation practices, and domain-specific deployment constraints. This survey addresses this gap by reviewing GenAI from both an algorithmic and cross-domain perspective. Following a PRISMA-inspired search and screening procedure, we organize representative and technically relevant studies across major GenAI families, including variational autoencoders, generative adversarial networks, diffusion models, autoregressive models, flow-based models, and recent foundation and multimodal models. We synthesize applications across healthcare, agriculture, manufacturing, transportation, earth and environmental systems, computer science and networks, materials science, finance and economics, education, business and services, and creative industries. The review shows that diffusion and autoregressive foundation models increasingly dominate high-fidelity image, language, and multimodal generation, while GANs, VAEs, and flow-based models remain important in data-limited, structured, scientific, and privacy-aware settings. Beyond summarizing applications, the paper compares domain-specific data structures, evaluation practices, robustness concerns, failure modes, and ethical risks. The survey contributes a unified reviewed abstraction of GenAI workflows, a comparison with prior surveys, a domain-aware synthesis of open gaps, and a future roadmap for trustworthy, validated, and responsible GenAI deployment.