Artificial Intelligence (AI) has become an integral component of modern healthcare, enabling automated diseasediagnosis,medical image interpretation, patient risk prediction, and clinical decision support. Despite the remarkable diagnostic performanceofmachine learning and deep learning algorithms, their limited transparency often restricts their acceptance in real-worldclinicalenvironments. Medical practitioners require not only highly accurate predictions but also understandable explanationsthatjustifydiagnostic recommendations. Explainable Artificial Intelligence (XAI) addresses this challenge by providing interpretablereasoningbehind AI-generated decisions, thereby improving clinician confidence, reducing diagnostic uncertainty, and facilitatinginformedmedical decision-making. This experimental study investigates the effectiveness of Explainable Artificial Intelligencetechniquesinenhancing the transparency and reliability of medical diagnosis systems. The study examines how interpretable machine learningmodels,rule-based reasoning mechanisms, feature importance analysis, and visualization techniques contribute to diagnostic accuracywhilemaintaining model interpretability. A systematic experimental framework is proposed to evaluate the relationship betweenexplainability,diagnostic performance, clinician trust, and decision reliability across multiple medical diagnostic scenarios. The study further introducesa mathematical framework and algorithmic strategy for measuring explanation quality, diagnostic confidence, and predictionconsistencywithin intelligent healthcare systems. Keywords: Explainable Artificial Intelligence (XAI), Medical Diagnosis Systems, Clinical Decision Support, MachineLearning,Interpretability