Explainable Artificial Intelligence (XAI) has emerged as a critical area of research and application as machine learning and deep learning systems permeate high-stakes domains such as healthcare, finance, and governance. While early artificial intelligence systems were often regarded as inscrutable black boxes, the need for transparency, accountability, and human trust has led to a proliferation of methods and frameworks intended to make AI decision-making understandable to diverse stakeholders. This article provides a comprehensive analysis of XAI research and practice, emphasizing theoretical foundations, bias mitigation, socio-technical interactions, and emerging directions in explainability research. We explore conceptual definitions of explainability, trace the historical evolution of explanation methods, and analyze how considerations of fairness and bias intersect with the imperative for explanation. Drawing upon extensive literature in machine learning interpretability, social science insights into explanation, and argumentation frameworks, we argue that achieving trustworthy AI demands both technical and human-centered advances. We also examine the role of explainability in human-AI interaction, its implications for trust and accountability, and the challenges in operationalizing transparent systems across contexts. Through detailed examination of current methods, stakeholder needs, and limitations, this article identifies key gaps in existing research, proposes integrative frameworks that bridge technical and social perspectives, and outlines future directions for XAI that address ethical, legal, and societal challenges. By synthesizing diverse strands of research, this article contributes to a nuanced understanding of how explanation functions not merely as a technical output but as a socio-technical process central to the responsible deployment of AI.