Artificial Intelligence (AI) is transforming telemedicine by helping doctors monitor patients remotely, make faster diagnoses, and offer personalized treatments. AI tools can study data from sensors, medical records, or wearable devices to detect early signs of illness. However, many AI models act like a “black box,” meaning their decisions are hard to understand. This lack of clarity makes doctors and patients hesitant to trust AI results. Explainable Artificial Intelligence (XAI) helps solve this problem. XAI makes AI systems transparent by showing how and why a decision is made. For example, if an AI system predicts that a patient might have heart disease, XAI can explain which factors such as heart rate or blood pressure led to that result. This improves trust, safety, and confidence in AI assisted healthcare. In telemedicine, XAI methods are used to explain results from medical imaging, remote monitoring, and chat-based health systems. Researchers test these models in clinical settings to check their accuracy and reliability. Evaluation methods often include comparing AI predictions with expert opinions to ensure explanations make sense to doctors. Despite its benefits, XAI faces challenges such as managing large medical data, protecting patient privacy, and ensuring explanations are simple for non-experts. For AI to be safely and ethically used in telehealth, it must be explainable and understandable. Explainable AI (XAI) builds trust between technology and people, leading to safer and more reliable healthcare.