"This project, titled SolarSync, focuses on optimizing rooftop solar photovoltaic (PV) systems by addressing the critical issue of shadow coverage in urban environments. In densely populated areas, shadows from surrounding buildings and structures dynamically change throughout the day and seasons, significantly impacting solar panel efficiency. Traditional PV placement models typically use static data, ignoring these dynamic shadow variations and resulting in suboptimal energy generation. To overcome this limitation, the project introduces a machine learning-powered system that leverages instance segmentation using the Mask R-CNN architecture for precise building and shadow detection. This is integrated with a dynamic shadow simulation model that estimates building heights and tracks sunlight exposure in real time using high-resolution satellite imagery. Together, these technologies enable accurate modeling of shadow behavior and allow for data-driven optimization of solar panel placement on rooftops. The solution was evaluated using rigorous performance metrics, achieving high segmentation accuracy with an F1-Score of 0.8976 and Average Precision (AP) of 67.8% at an IoU threshold of 0.5. SolarSync sets a new standard in rooftop solar PV optimization by offering a scalable, globally adaptable tool that enhances the efficiency and sustainability of urban solar energy systems."