An oil spill at sea represents a catastrophic environmental event resulting from the release of oil into marine ecosystems. These incidents pose substantial risks to marine biodiversity, wildlife habitats, and coastal populations, often engendering enduring and widespread repercussions. Cleaning up oil spills is costly due to logistical challenges. Accurate measurement of spill characteristics like volume, thickness, and area of spill is crucial before deploying clean-up crews to optimize resource allocation and reduce expenses. The main objective of this research is to use computer vision to detect oil spills and estimate its thickness, helping in decision-making processes to clean up the spill area. A system architecture proposed in this study integrates a drone equipped with a camera and GSM module to inspect sea areas and capture images. These images are processed using a deployed computer vision segmentation model to detect oil spills and estimate oil thickness. Predicted results helps in decision-making via a dedicated application by applying predefined criteria to determine the thickness of the spill which further help in taking actions for removal of oil spills. The computer vision model developed in this research could detect and estimate oil thickness with a 94% accuracy. The proposed system in this study uses instance segmentation to detect and segment oil spills in drone footage. This computer vision-based approach accurately identifies and outlines oil spill areas, aiding in the selection of efficient cleanup strategies. Real-time monitoring and assessment capabilities enable quick decision-making and effective response measures.