In this paper, we examine the expanding potential of autonomous AI (AAI) systems, which are capable of making decisions independent of direct programming or past training. As this development enhances the potential of AI, it also introduces issues concerning trust, transparency, and successful human-AI interaction. Author discusses the role of Explainable AI (XAI) in enabling users to comprehend AI decisions, establish trust, and identify erroneous predictions. Our research surveys previous work on interpretability, usability, and the effects of XAI on human decision-making. Author also tackle the explainability problem in contemporary machine learning models like deep neural networks, which tend to act as "black boxes." This paper is a contribution to the ongoing quest for developing more transparent, trustworthy, and human-focused AI systems