In the realm of artificial intelligence (AI), the quest for creating highly accurate and sophisticated machine learning models has led to remarkable achievements across various domains. These AI models have demonstrated unparalleled capabilities in tasks like image recognition, natural language processing, and autonomous decision-making. However, the growing complexity of AI models, particularly deep learning models, has raised concerns about their lack of transparency and interpretability. As AI systems become increasingly integrated into critical applications, such as healthcare, finance, and autonomous systems, the need for Explainable AI (XAI) becomes more pronounced.