Artificial Intelligence has transitioned significantly from being a mere subject within Computer Science to becoming a distinct field of study of its own. The journey has started from basic logic designing and traditional programming to soft computing algorithms to sophisticated machine learning techniques, encompassing smaller artificial neural networks and advancing toward an epitome of deep learning methodologies. However, a notable challenge has emerged: the problem of transparency in many of the AI systems, which obscures their decision-making processes. This opacity acts like a block to the full realization of AI’s potential and its applications. As an approach of mitigating such concerns, explainable AI (XAI) is seen to be the ultimate game changer in this modern world of digital connectivity. Explainable AI intend to provide insightful deliveries of information refining better judgment that is easier to grasp by humans, clarifying the reasoning and the perspectives. It is observed to have an increase in influx of many research in recent times to have focal point on AI due to its promise of delivering results that are transparent, accountable, unbiased, authentic, and ethically sound. This paper also surely tries to capture the various aspects of XAI, with special focus on its existing taxonomies, current status and trends, application areas, opportunities, and limitations, and most importantly where it is going. This paper analyzes the papers published post-COVID and tries to bring out the visible as well as soft shift of paradigm in different fields like healthcare, finance, education, AI itself too.