Intelligent healthcare diagnosis has become a major research area due to the rapid growth of electronic healthrecords, medicalimaging systems, laboratory information systems, and clinical decision support technologies. The increasing volume andcomplexityofhealthcare data have created significant challenges for accurate disease diagnosis, timely clinical decision-making, andpersonalizedpatient care. Machine learning techniques have emerged as effective computational approaches for assisting healthcare professionalsbyautomatically identifying diagnostic patterns from large-scale medical datasets. This experimental study evaluates the performanceofwidely used machine learning models for intelligent healthcare diagnosis by comparing their diagnostic accuracy, precision, recall,F1-score, computational efficiency, and prediction reliability. The proposed framework integrates healthcare data preprocessing, featureselection, machine learning classification, and diagnostic performance evaluation into a unified analytical architecture. Amathematicalframework and algorithmic strategy are developed to assess classification effectiveness, resource utilization, diagnostic consistency,andpredictive performance. Experimental evaluation demonstrates that supervised machine learning algorithms significantlyimprovediseasediagnosis while exhibiting different performance characteristics across various healthcare datasets and diagnostic scenarios. Theproposedframework provides valuable guidance for researchers, healthcare practitioners, and clinical decision support systemdevelopersseekingto design accurate, reliable, and computationally efficient intelligent healthcare diagnosis systems. Keywords: Machine Learning, Intelligent Healthcare Diagnosis, Clinical Decision Support, Disease Prediction, Medical DataMining.