Papilledema is a medical disorder marked by the enlargement of the optic disc. Optic disc imaging is essential for the diagnosis of papilledema, as neglecting to perform this procedure can lead to fatal outcomes. This research presents a novel approach that combines deep learning with the Harris Hawks Optimization (HHO) algorithm to increase the accuracy of diagnosing and distinguishing papilledema in optical disc images. The proposed technique presented in this study focuses on optimizing the weights of the convolutional neural network (CNN) model. This optimization process improves model training by using the underlying optimization principles. The technique was evaluated using the Kaggle dataset, which was made available for this purpose. The evaluation results showed that the proposed technique achieved an accuracy of 0.997%, surpassing the performance of existing techniques such as VGG16, DenseNet121, EfficientNetB0, and EfficientNetB3. The proposed model demonstrates that state-of-the-art CNN models, when paired with the HHO algorithm, can reliably diagnose real papilledema, pseudo-papilledema, and normal optic discs. This could potentially save lives for patients.