Explainable AI (XAI) is crucial for making the complex and often opaque decision-making processes of AI models more transparent and understandable, thereby fostering trust and compliance with regulatory and ethical standards. As AI systems, particularly those based on deep learning architectures, become more intricate and capable, they are often perceived as "black boxes" due to their non-linear, multi-layered structures. This complexity can obscure how models derive their outputs, raising issues regarding transparency, trust, and bias, especially in high-stakes fields like healthcare and finance. Explainability encompasses global and local explanations—understanding the overall model behavior and the reasoning for specific outputs, respectively—using techniques such as SHAP, LIME, and partial dependence plots to make AI more interpretable. By adopting such methods, organizations can ensure responsible AI development, improve model performance, and meet the diverse needs of stakeholders while navigating the challenges of integrating explainability into existing workflows and addressing potential biases. As AI continues to evolve, the trend towards implementing explainable systems is expected to grow, emphasizing the balance between model complexity and interpretability to uphold ethical standards and enhance stakeholder confidence.