The increasing need for green energy has further pushed the search for urban solar potential, especially on building walls. Conventional methods of solar assessment are hardly accurate when it comes to measuring vertical surfaces, due to shading, orientation, and other architectural challenges. This paper offers a novel approach through the integration of high-resolution satellite images, deep learning, and simulations in MATLAB/Simulink for the evaluation of façade solar energy efficiency. The Faster R-CNN model was employed for window detection and calculation of the window-to-wall ratio (WWR) which can be used for automated façade analysis. Energy yield, performance ratio (PR), and specific production results were all evaluated with PVsyst simulation software for vertical photovoltaic (PV) System. Results indicated an 83.22% PR and specific yield of 626 kWh/kWp/year; thus, real-world feasibility can be justified for façade-integrated PV systems. The proposed methodology offers a scalable solution for urban solar planning, supporting net-zero energy objectives.