Artificial intelligence (AI) has become an important tool for recognizing changes in the ocular fundus, but most existing studies are conducted in peacetime clinical environments with advanced diagnostic equipment and stable infrastructure. In contrast, wartime conditions impose severe constraints, including limited access to sophisticated imaging devices, reduced medical resources, and the urgent need for rapid decision-making. This article addresses this research gap by examining AI-assisted classification of retinal fundus images collected under conflict conditions in Ukraine. Three approaches were employed: feature extraction combined with deep neural networks, convolutional neural network (CNN)-based models, and Microsoft’s Custom Vision platform. The dataset consisted of 448 retinal images divided into five groups: normal findings, trauma-related injuries, optic nerve disc changes, vascular lesions, and macular degeneration. Despite the small and imbalanced dataset, and the challenging acquisition environment, each pre-processing method achieved at least 80% classification accuracy, with the CLAHE method yielding the best results. This study demonstrates, for the first time, that AI can provide reliable ophthalmic diagnostics in extreme and resource-limited wartime settings, bridging the gap between peacetime and conflict healthcare.