In recent times, Artificial Intelligence (AI) has been evolving globally in various disciplines. To reduce human efforts, automation is necessary for which AI interpretability, analytical processing and explanation for the result is essential which is termed as Explainable AI (XAI). The present study explains the historical aspects of AI's birth from traditional databases, and flat files to the era of Cognitive Science in the multifaceted world which highlights the emerging field of XAI. Traditionally the concept of the black box was applied to understand how XAI impacts the AI decision-making processes. Various fields such as finance, healthcare, and autonomous driving were illustrated to understand XAI. Real-world case studies highlight the application of XAI in many organizations to enhance AI for fairness and transparency. Furthermore, the paper explores the research challenges, issues and opportunities of XAI, emphasizing its critical role in developing trust and ensuring fairness in AI applications. It highlights the importance of XAI in shaping the future of AI and fostering responsible development across industries.