Glaucoma is a progressive optic neuropathy that results in vision loss if not detected and treated promptly. This condition is caused by the degeneration of retinal ganglion cells. Traditional methods are subjective and time-consuming. Image processing techniques and supervised machine learning algorithms can enhance glaucoma diagnosis accuracy, efficiency, and reliability. This review explores the potential of image processing techniques and supervised machine learning algorithms in glaucoma diagnosis. It highlights challenges like image quality variability and algorithmic bias. The review suggests that this combination could revolutionize early detection and management of this debilitating disease, providing a non-invasive, efficient tool.