Machine learning has become a pivotal tool in automating image processing by interpreting visual data similarly to the human brain, with applications spanning from facial recognition to self-driving cars. Image processing, which involves analyzing and extracting useful information from images, has evolved significantly, employing both analog and digital methods. Digital image processing is particularly enhanced by machine learning and deep learning techniques, enabling detailed tasks like pattern recognition and medical imaging. Essential to these processes are various frameworks such as OpenCV, TensorFlow, and PyTorch, which provide robust tools for developing machine learning models. Among these, Convolutional Neural Networks (CNNs) stand out for their ability to accurately identify image features through layered architectures comprising convolutional, pooling, and fully connected layers. These advancements have facilitated the creation of high-performing models capable of transforming image data into actionable insights, with diverse applications including classification, segmentation, and beyond.