Human-AI collaboration (HAC) is transforming various industries by integrating artificial intelligence into collaborative frameworks where AI systems and humans work together to achieve common goals. This paper is focused on understanding human-AI collaboration, starting with the fundamental principles and the frameworks or theories behind human-in-the-loop (HITL) and human-on-the-loop (HOTL) systems, decision-making augmentation, and collaborative reinforcement learning. It also describes the major technologies and tools that are employed in human-AI collaboration (HAC) in different sectors like healthcare, manufacturing, retail, and defense. The research also sheds light on different ethical, technical, and practical human-AI collaboration (HAC) challenges such as AI interpretability, trust, bias, and human cognitive load management. Lastly, the future of adaptive AI models and AI-powered decision-making frameworks along with human-centric AI applications are discussed.