This work introduces a novel artificial intelligence–driven framework designed to personalize and optimize antimalarial therapies by integrating nutritional biomarkers, phytotherapeutic bioactives, and pharmacological data. The study develops predictive machine learning models capable of identifying the key physiological, metabolic, and therapeutic factors that influence individual treatment outcomes. By combining multi-omics insights, clinical data, and evidence-based phytotherapy, this research provides a unified computational approach for drug–nutrient–phytochemical interaction modeling. The objective is to shift from standardized malaria treatment protocols toward adaptive, precision-based therapeutic strategies tailored to the patient’s biological profile. This integrative methodology represents a significant innovation in malaria research, pharmacology, nutrition science, and African traditional medicine. The dataset, conceptual models, and methodological contributions presented here support the development of personalized malaria treatments, reduce the risk of drug resistance, and open new avenues for applying AI in infectious disease management.