  In recent years, the study of artificial intelligence (AI) has undergone a
paradigm shift. This has been propelled by the groundbreaking capabilities of
generative models both in supervised and unsupervised learning scenarios.
Generative AI has shown state-of-the-art performance in solving perplexing
real-world conundrums in fields such as image translation, medical diagnostics,
textual imagery fusion, natural language processing, and beyond. This paper
documents the systematic review and analysis of recent advancements and
techniques in Generative AI with a detailed discussion of their applications
including application-specific models. Indeed, the major impact that generative
AI has made to date, has been in language generation with the development of
large language models, in the field of image translation and several other
interdisciplinary applications of generative AI. Moreover, the primary
contribution of this paper lies in its coherent synthesis of the latest
advancements in these areas, seamlessly weaving together contemporary
breakthroughs in the field. Particularly, how it shares an exploration of the
future trajectory for generative AI. In conclusion, the paper ends with a
discussion of Responsible AI principles, and the necessary ethical
considerations for the sustainability and growth of these generative models.
