Malaria is also a continuous worldwide health risk, claiming over 600,000 lives and over 240 million cases every year. With such life-risking figures, timely and prompt treatment necessitates effective and timely diagnosis. The current paper proposes a hybrid model incorporating Ant Colony Optimization (ACO) and Gray Wolf Optimizer (GWO) for malaria prediction from blood smear images. The model is superior to ACO segmentation for feature selection and GWO for classification because it yields improved results. The model was applied to 10,000 data of blood smear images and achieved a staggering 98.5% accuracy, 97.8% sensitivity, and 98.9% specificity. The above tablatures are superior to the traditional approach, reflecting the efficiency of performance of the hybrid model. The ACO-GWO malaria diagnosis technique has high potential for application in future computer-aided diagnosis, particularly in limited-resource health conditions. It minimizes errors in diagnosis and leads to improved health, therefore a great improvement of anti-malaria programs, particularly where quality healthcare is not readily available.