Gopal Singh Panwar's article highlights eight prominent Python libraries used for image processing in machine learning, emphasizing their efficiency in handling large datasets of digital images. OpenCV, an open-source library developed by Intel, is noted for tasks such as object detection and image segmentation, while Scikit-Image offers a Python-based platform for algorithms including segmentation and filtering. SciPy, primarily known for scientific computations, provides multi-dimensional image processing capabilities, and Pillow (PIL) supports tasks like reading and rescaling images. NumPy, a fundamental package for numerical computations, facilitates image manipulation through pixel value adjustments. Mahotas, designed for bioimage informatics, excels in operations like template matching, while SimpleITK provides a robust platform for image segmentation and registration. Finally, Pgmagick, a GraphicsMagick binding for Python, offers utilities for image resizing and rotation, making these libraries essential tools for data scientists in preprocessing images for machine learning models.