Generative AI technology now leads the way as a transformative innovation that produces diverse, realistic content across various content modalities. This research extensively reviews generative AI by examining its core mechanisms, architectural improvements, and current breakthroughs in making images, text, audio, and videos. The paper discusses three main generative models, GANs, VAEs and diffusion models, to explain their distinctive advantages and technical constraints. The document demonstrates industrial uses in different sectors yet focuses on essential issues connected to biased data, unintelligible model configurations, and efficiency constraints. The paper focuses on upcoming developments of multimodal systems and environmentally friendly AI practices. The main emphasis lies in building generative models that demonstrate robustness, interpretability, and sustainability across the environment. The paper explores research directions for the future with a specific focus on integrating multiple modalities and establishing ethical practices during model design. Researchers, practitioners, and policymakers will find this review highly helpful in their tasks related to understanding and developing generative AI technologies.