Artificial Intelligence (AI), a multidisciplinary field born over six decades ago, integrates scientific principles, theoretical foundations, and technical methodologies to replicate human cognitive functions. Despite its impressive advancements, a persistent and critical barrier remains: the lack of interpretability in many AI systems. These systems frequently operate as opaque “black boxes,” capable of producing highly accurate predictions without offering insight into their decision-making processes. This opacity poses a significant challenge to trust and accountability. In response, the emerging field of Explainable Artificial Intelligence (XAI) has garnered increasing attention for its promise to bridge this gap by enhancing the transparency and reliability of AI models. Recognized as essential for sustaining long-term progress in AI, explainability is no longer optional but imperative. This paper provides a foundational exploration for scholars and practitioners aiming to understand the core components and evolving methodologies within the XAI landscape. It surveys key developments, identifies prevailing trends, and highlights pivotal research directions shaping the future of this transformative domain.