Age-related macular degeneration (AMD) is a main reason for imaginative and prescient loss among older adults worldwide. Early detection and intervention are essential for coping with this modern eye disease. This has a look at explores the software of gadgets getting to know strategies for the automatic detection of AMD from retinal fundus images. We evaluate the overall performance of three algorithms: Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and Random Forests (RF). A dataset of 5,000 high-decision retinal images, similarly, dispensed among AMD and non-AMD cases, turned into used for schooling and evaluation. Preprocessing strategies, together with photo resizing, evaluation enhancement, and statistics augmentation, have been carried out to enhance version overall performance. The CNN version established advanced overall performance, accomplishing an accuracy of 96.8%, sensitivity of 95.7%, and specificity of 97.9%. The SVM and RF fashions additionally confirmed promising outcomes with accuracies of 92.3% and 94.1%, respectively. We similarly analyzed the fashions’ overall performance throughout distinct AMD levels and investigated the interpretability of the CNN version the usage of Gradient-weighted Class Activation Mapping (Grad-CAM). Our findings propose that gadgets getting to know, especially deep getting to know strategies, can notably resource withinside the early detection of AMD, doubtlessly enhancing affected person results via well-timed intervention and management.