Explainable Artificial Intelligence (XAI) is a young and rapidly developing field with a critical mission to enhance transparency, trust and accountability in AI systems that often rely on black-box decision-making models. As AI models continue to be deployed at scale in day-to-day applications, and as existing systems are refined for even greater accuracy, their use in high-stakes domains such as healthcare, finance and legal decision-making underscores the urgent need for clear frameworks to establish transparency and trust. In this chapter, we review the core tools and techniques that define the state of XAI. We discuss LIME, SHAP, Counterfactual Explanations and Partial Dependence Plots under model-agnostic approaches, along with Saliency Maps, Layer-wise Relevance Propagation, Attention Mechanisms and Rule Extraction methods under model-specific approaches. We also address emerging challenges such as scalability, explanation fidelity and fairness in explanation. By presenting these methods alongside current limitations and research directions, this chapter aims to provide both emerging and seasoned professionals with a structured understanding of the XAI landscape and a foundation to guide future research and practice.