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This study investigates the application of supervised machine learning algorithms—specifically Random Forest, Support Vector Machine, and Gradient Boosting—for early-stage malaria detection using clinical symptom datasets collected from rural health centres in Kwara State, Nigeria. The models were trained on 3,200 anonymised patient records. Random Forest achieved 94.2% accuracy, outperforming benchmark methods.