This paper presents a systematic review and analysis of recent advancements and applications in Generative Artificial Intelligence (GenAI), focusing on its impact in various domains such as image translation, medical diagnostics, natural language processing, and video synthesis. The review highlights the key techniques and models in GenAI, including Generative Adversarial Networks (GANs), Transformers, Variational Autoencoders (VAEs), and Diffusion models. It discusses the evolution of these models, their applications, and the challenges they face. The paper also emphasizes the importance of Responsible AI principles and ethical considerations in the development and use of generative models. The review covers the historical development of GenAI, starting from the early 2012-2018 period, and highlights the significant advancements made in the field since then. It discusses the key applications of GenAI, such as image translation, video synthesis, and natural language processing, and provides a detailed analysis of the performance of various models in these areas. The paper also explores the challenges and opportunities associated with GenAI, including issues such as training instability, model collapse, and the need for ethical considerations in the development of these models. The paper discusses various techniques and models in GenAI, including GANs, Transformers, VAEs, and Diffusion models, and their applications in different fields. It highlights the performance of these models in tasks such as image translation, video synthesis, and natural language processing, and provides a detailed analysis of their effectiveness. The paper also emphasizes the importance of ethical considerations and responsible AI practices in the development and use of generative models. The review concludes with a discussion on the future directions of GenAI, emphasizing the need for continued research and development in this field. The paper also highlights the importance of ethical considerations and responsible AI practices in the development and use of generative models. The review provides a comprehensive overview of the current state of GenAI, its applications, and the challenges it faces, and emphasizes the need for continued research and development in this field.This paper presents a systematic review and analysis of recent advancements and applications in Generative Artificial Intelligence (GenAI), focusing on its impact in various domains such as image translation, medical diagnostics, natural language processing, and video synthesis. The review highlights the key techniques and models in GenAI, including Generative Adversarial Networks (GANs), Transformers, Variational Autoencoders (VAEs), and Diffusion models. It discusses the evolution of these models, their applications, and the challenges they face. The paper also emphasizes the importance of Responsible AI principles and ethical considerations in the development and use of generative models. The review covers the historical development of GenAI, starting from the early 2012-2018 period, and highlights the significant advancements made in the field since then. It discusses the key applications of GenAI, such as image translation, video synthesis, and natural language processing, and provides a detailed analysis of the performance of various models in these areas. The paper also explores the challenges and opportunities associated with GenAI, including issues such as training instability, model collapse, and the need for ethical considerations in the development of these models. The paper discusses various techniques and models in GenAI, including GANs, Transformers, VAEs, and Diffusion models, and their applications in different fields. It highlights the performance of these models in tasks such as image translation, video synthesis, and natural language processing, and provides a detailed analysis of their effectiveness. The paper also emphasizes the importance of ethical considerations and responsible AI practices in the development and use of generative models. The review concludes with a discussion on the future directions of GenAI, emphasizing the need for continued research and development in this field. The paper also highlights the importance of ethical considerations and responsible AI practices in the development and use of generative models. The review provides a comprehensive overview of the current state of GenAI, its applications, and the challenges it faces, and emphasizes the need for continued research and development in this field.