Accurate predictive models in medical diagnosis are vital for improving patient outcomes and enhancing the efficiency of healthcare delivery. This paper provides a comprehensive comparative analysis of conventional and advanced machine learning algorithms for multi-disease prediction, focusing on three distinct conditions: diabetes, heart disease, and Parkinson’s disease. Six algorithms are implemented and assessed in three pairs: Logistic Regression is contrasted with Random Forest for diabetes prediction, Decision Tree is compared to Gradient Boosting for heart disease classification, and Support Vector Machine is evaluated against AdaBoost for Parkinson’s disease detection. Performance is measured using accuracy, precision, recall, and F1-score with rigorous statistical validation through 10-fold cross-validation and significance testing. The findings challenge the common assumption that advanced algorithms universally surpass conventional ones in medical diagnostics. Notably, traditional algorithms yielded competitive or superior results in several disease contexts. Support Vector Machine achieved near-perfect recall of 100% for Parkinson’s prediction, while Logistic Regression demonstrated competitive performance in diabetes classification with superior interpretability. A web-based clinical decision support system was developed and deployed to demonstrate practical implementation of the models. These outcomes highlight the importance of domain-specific algorithm evaluation in medical AI and question conventional assumptions about algorithmic performance.