Explainable Artificial Intelligence represents a fundamental shift in the design and deployment of intelligent systems toward greater transparency, interpretability, and accountability. As artificial intelligence increasingly drives decisions in healthcare, finance, governance, and everyday consumer technologies, concerns have risen regarding opaque decision-making, algorithmic bias, and ethical responsibility. XAI seeks to bridge the gap between highly complex machine learning algorithms and human understanding by providing mechanisms through which users can interpret, question, and trust the outcomes generated by AI models. The abstract nature of deep learning models such as neural networks makes it difficult to trace causal reasoning, leading to what many scholars describe as the “black-box” problem. Explainable systems attempt to mitigate this challenge by incorporating transparency frameworks, human-centered design principles, and ethical guidelines that promote interpretability without compromising accuracy. Keywords such as interpretability, transparency, accountability, bias mitigation, and ethical AI underscore the growing importance of human oversight in computational systems. The emergence of regulatory frameworks such as the European Union’s General Data Protection Regulation and the AI Act highlights global recognition of explainability as a core requirement for trustworthy AI. This research explores the conceptual foundations, theoretical developments, and methodological approaches that advance the cause of explainable AI, emphasizing its role in ensuring fairness, safety, and public trust across domains.