This chapter examines the transformative evolution from traditional artificial intelligence systems to generative AI models, exploring their foundational architectures, diverse applications, and emerging challenges. We analyse key generative modelling approaches, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), alongside emerging paradigms such as diffusion models, transformer-based systems, and multimodal architectures. The chapter discusses technical implementation considerations, evaluates applications across creative industries and scientific domains, and addresses critical ethical challenges, including deepfake proliferation, misinformation risks, and intellectual property concerns. Through comprehensive industry case studies and analysis of future technological directions, we demonstrate how generative AI augments human creativity while necessitating robust ethical frameworks for responsible development and deployment.