The use of machine learning (ML) is growing very rapidly in the field of medical science. ML can simply be defined as a task which is implemented by a computer or a robot with no or minimal human involvement. The use of machine learning in the medical field will help in mass screening and also help in providing an accurate diagnosis for a particular disease. The accessibility, quality as well as affordability of the healthcare system can be increased by using ML. Machine learning can perform complex calculations which help in pattern recognition that creates relations between the input data and the standard values. This comparison helps medical professionals to give near to proper diagnosis to the patient. Al-assisted screening and diagnosis based on images is an evolving technology, this technology can be used in ophthalmology for the treatment and diagnosis of diabetes retinopathy. Diabetes retinopathy (DR) is an ever-increasing problem. It is the leading cause of visual impairment globally. The cases of visual impairment and blindness due to DR were around 2.5 million in 2015 and are expected to increase up to 3.8 million cases worldwide by 2022. Though it is expected that the number of cases of DR is likely to decrease in high-income countries the only way to cure DR in middle-income and low-income countries is only by early detection and treatment. Diabetes retinopathy can only be cured if timely treatment is provided to the patient. The best way to provide early screening and treatment for DR to the patient is to use machine learning. Machine learning can help in early screening as well as diagnosis of a diabetes retinopathy and also helps in reducing the number of trained humans required for the same hence ML proves to be very advantageous for the patients as well as the ophthalmologist.