This paper provides a systematic review and analysis of recent advancements and techniques in Generative Artificial Intelligence (GenAI), focusing on their applications and performance. The authors highlight the paradigm shift in AI driven by generative models, particularly in supervised and unsupervised learning scenarios. Key applications discussed include image translation, medical diagnostics, textual imagery fusion, and natural language processing. The paper also explores specific models such as Generative Adversarial Networks (GANs), Transformers, Variational Autoencoders (VAEs), and Diffusion models, detailing their architectures, challenges, and advancements. Additionally, the paper addresses ethical considerations and responsible AI development, emphasizing the importance of sustainable and ethical practices in the field. The review covers significant developments from 2018 to 2023, providing a comprehensive overview of the current state and future directions of GenAI.This paper provides a systematic review and analysis of recent advancements and techniques in Generative Artificial Intelligence (GenAI), focusing on their applications and performance. The authors highlight the paradigm shift in AI driven by generative models, particularly in supervised and unsupervised learning scenarios. Key applications discussed include image translation, medical diagnostics, textual imagery fusion, and natural language processing. The paper also explores specific models such as Generative Adversarial Networks (GANs), Transformers, Variational Autoencoders (VAEs), and Diffusion models, detailing their architectures, challenges, and advancements. Additionally, the paper addresses ethical considerations and responsible AI development, emphasizing the importance of sustainable and ethical practices in the field. The review covers significant developments from 2018 to 2023, providing a comprehensive overview of the current state and future directions of GenAI.