This narrative review discusses the current and future screening programs for diabetic retinopathy (DR), a serious complication of diabetes. The review highlights the global prevalence of diabetes, which is expected to rise, increasing the need for effective DR screening. High-income countries have largely implemented national DR screening programs, while low- and middle-income countries face challenges in implementation. The review emphasizes the importance of screening methods that are cost-effective, accessible, and accurate, such as using handheld retinal cameras and artificial intelligence (AI) algorithms for detection. The review outlines the use of various screening methods, including fundus imaging, visual acuity tests, and classification scales. It discusses the limitations of traditional seven-field stereoscopic photography and the potential of alternative methods like smartphone-based retinal imaging. AI has shown promise in improving the accuracy and efficiency of DR screening, particularly in resource-limited settings. The review also addresses the importance of individualized screening intervals based on DR severity and the need for integrated screening and treatment strategies to effectively manage DR. Challenges include the lack of standardized screening programs in many countries, limited access to trained professionals, and the impact of economic factors on screening availability. The review concludes that while there are promising advancements in DR screening technologies, there is a need for further research and implementation of AI-based solutions to improve screening effectiveness and accessibility globally. Ethical considerations, such as data privacy and algorithmic bias, must also be addressed to ensure the responsible use of AI in DR screening.This narrative review discusses the current and future screening programs for diabetic retinopathy (DR), a serious complication of diabetes. The review highlights the global prevalence of diabetes, which is expected to rise, increasing the need for effective DR screening. High-income countries have largely implemented national DR screening programs, while low- and middle-income countries face challenges in implementation. The review emphasizes the importance of screening methods that are cost-effective, accessible, and accurate, such as using handheld retinal cameras and artificial intelligence (AI) algorithms for detection. The review outlines the use of various screening methods, including fundus imaging, visual acuity tests, and classification scales. It discusses the limitations of traditional seven-field stereoscopic photography and the potential of alternative methods like smartphone-based retinal imaging. AI has shown promise in improving the accuracy and efficiency of DR screening, particularly in resource-limited settings. The review also addresses the importance of individualized screening intervals based on DR severity and the need for integrated screening and treatment strategies to effectively manage DR. Challenges include the lack of standardized screening programs in many countries, limited access to trained professionals, and the impact of economic factors on screening availability. The review concludes that while there are promising advancements in DR screening technologies, there is a need for further research and implementation of AI-based solutions to improve screening effectiveness and accessibility globally. Ethical considerations, such as data privacy and algorithmic bias, must also be addressed to ensure the responsible use of AI in DR screening.