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The manual inspection of concrete structures, such as tall buildings, bridges, and huge infrastructures can be time-consuming and costly, and damage assessment is a crucial task that requires the close-range inspection of all surfaces. The proposed system uses computer vision model to identify various types of damages on these structures. The computer vision model and was trained on a large dataset of drone footage, which was annotated manually to ensure accuracy. The model was then tested on new data, and the results showed that it could accurately detect and identify structural damage on concrete structures with a 94% accuracy. The system is much faster and more efficient than manual inspection, reducing the time and cost required for damage assessment. The proposed system has the potential to revolutionize the way we perform damage assessment on concrete structures. It can help to preserve and protect these valuable assets by enabling the early detection of damage and facilitating timely repairs.