The high-speed growth and development of artificial intelligence (AI) have profoundly influenced numerous industries. However, the increasing complexity and lack of transparency in many AI models, often termed “black-box” systems, have raised significant concerns regarding their trustworthiness and accountability. In response, the field of Explainable AI (XAI) has emerged as an important area of research, focusing on enhancing the transparency and interpretability of AI systems for human users. Ethical considerations surrounding XAI are also addressed, with attention to issues such as bias, fairness, and accountability. The evolution of XAI, its core principles, and the most recent techniques in the field is a vital area of growth. Exploring the key concepts and methods, including both model-agnostic and model-specific approaches, strategies for evaluating feature importance, and techniques for causal inference is covered in most sections of the paper. Also highlighting XAI’s applications across various domains such as healthcare, finance, autonomous systems, and natural language processing is done. In each sector, it discusses the specific challenges and potential advantages of implementing XAI, demonstrating how it can improve decision-making processes and foster greater trust in AI systems.