As Artificial Intelligence (AI) is an integral part of present-day intelligent healthcare systems, ensuring transparency and trustworthiness in AI-driven decisions is paramount. Explainable AI (XAI) enhances the interpretability of complex models, strengthening trust and accountability in healthcare applications. XAI achieves this by clarifying how AI systems generate predictions and recommendations. This review comprehensively analyzes XAI methods and highlights their importance in intelligent healthcare, where decision-making profoundly impacts critical outcomes. Many problems arise with healthcare data, including differences, privacy concerns, and the need for personalized care. This document discusses essential techniques of XAI, including model-agnostic methods, interpretable deep learning models, and visualization tools, emphasizing their applicability in clinical settings. In addition, this document examines the function of XAI in facilitating collaboration between clinicians and AI, enhancing patient engagement, and helping to ensure regulatory compliance. This study highlights the importance of explainability in building trustworthy and ethically sound AI systems for healthcare, paving the way for better patient outcomes and advancing innovative healthcare solutions.