Age-related Macular Degeneration (AMD) is a leading cause of severe visual impairment and blindness in developed countries, particularly affecting individuals over the age of 65. In this study, we propose a novel AI-driven approach that leverages sequential optical coherence tomography (OCT) images to detect neovascular activity and monitor disease evolution in AMD patients. By analyzing paired OCT scans, our model is designed to identify subtle changes indicative of disease worsening or improvement, enabling more precise and individualized treatment planning. This approach was rigorously tested in the Monitoring Age-related Macular Degeneration Progression In Optical Coherence Tomography for Task 1, part of the MICCAI 2024 challenge, where it achieved second place in the preliminary round, demonstrating its efficacy and potential in clinical applications. The results underscore the model’s ability to enhance the decision-making process in AMD management, paving the way for more effective use of anti-Vascular Endothelial Growth Factor (VEGF) therapy therapy.