The human eye, often considered the most crucial sense organ for perceiving and understanding the world, plays a fundamental role in our ability to see and interpret our surroundings. However, vision can be significantly impacted by various ocular eye disorders, which pose significant challenges to our visual capabilities. Among these disorders, one of the most prevalent conditions is cataract, a condition characterized by cloudiness in the lens of the eye, resulting in impaired vision and a blurred perception of objects. Typically, cataracts are more commonly found in individuals of advanced age due to natural aging processes. Identifying and diagnosing ocular eye illnesses can be a complex and challenging task, often requiring sophisticated techniques and tools to aid in the process. In recent research endeavors, automated learning algorithms have emerged as valuable tools for assisting in the detection of ocular eye disorders. These algorithms, such as Convolutional Neural Networks (CNNs), are employed alongside image pre-processing techniques to analyze and interpret ocular data. By leveraging the power of machine learning, researchers have been able to develop and refine predictive models for ocular diseases. To evaluate the accuracy of disease prediction models, various design pattern graphs can be utilized. These graphs serve as visual representations, showcasing the performance and reliability of the algorithms in predicting ocular eye disorders. By employing these advanced technological approaches, researchers and medical professionals can enhance their ability to identify and diagnose ocular conditions, ultimately leading to more effective treatments and interventions for individuals affected by these diseases.