The process of assigning colors represents a fundamental component of computer vision techniques which enables successful execution of object recognition along with image segmentation and automated color-based identification. This approach encounters difficulties when they need to classify colors under different lighting conditions and complex background environments. The research evaluates deep learning color classification methods that combine K-Nearest Neighbors (KNN) with Artificial Neural Networks (ANN). The entire system starts with data cleaning then proceeds to extract color features with histograms and trains models while OpenCV executes live color identification. A comparative evaluation examines both KNN and ANN systems through their strengths and weaknesses when it comes to accuracy and efficiency as well as their performance stability in changing illumination environments. The achieved results show deep learning effectively enhances color classification performance which establishes it as an operational solution for current industry needs.