Malaria remains a significant global health threat, causing millions of infections and deaths annually, especially in sub-Saharan regions of Africa. Malaria diagnosis using artificial intelligence (AI) can potentially improve the recording, reading, and accuracy of malaria diagnostics results. However, explainability in these AI models is crucial for ensuring their reliability and understanding of the decision-making processes. This research focuses on leveraging Grad-CAM (Gradient-Weighted Class Activation Mapping) to enhance the explainability of malaria microscopy results from AI models. Grad-CAM effectively visualizes the regions of a microscopic image most influential in an AI model’s decision, highlighting parasitic cells and providing insights into the model’s reasoning. To demonstrate Grad-CAM’s effectiveness, we apply it and its variants to several powerful deep learning CNN architectures, including EfficientNet of validation accuracy 99.44%, ResNet of validation accuracy 99.81%, Inception-V3 of validation accuracy 89.37%, and DenseNet of validation accuracy 89.37%. Our results demonstrate Grad-CAM, Smooth Grad-CAM, Vanilla, and Grad-CAM++ abilities that enhance explainability improve diagnostic performance, and support the development of trustworthy AI-powered malaria detection systems with real-world impact in resource-constrained environments.