Explainable Artificial Intelligence (XAI) has risen as a pivotal advancement in dealing with the challenges of interpretability and transparency in AI-driven healthcare systems. AI’s rapid integration into healthcare has shown immense potential in diagnostics, prognosis, personalized medicine, and decision-making. However, the absence of proper explainability in traditional AI models has raised critical concerns regarding trust, ethical accountability, and clinical adoption. The research paper examines both XAI methodologies and healthcare applications in present scenario, emphasizing how these techniques help increase the interpretability in case of complex models without compromising predictive accuracy. It delves into the pivotal part of XAI in improving clinical decision support systems, risk stratification, and patient engagement, while also addressing regulatory compliance and ethical considerations. By analyzing recent advancements, challenges, and future prospects, this paper provides insights into how XAI can bridge the gap between real-world healthcare applications and AI innovations, cultivating trust while enabling safer, more effective healthcare delivery.