Eye disease affects 2.2 billion people globally, which in turn adversely affects schooling, economic productivity, and participation in social life. The primary conditions contributing to visual impairment and blindness include cataracts, age-related macular degeneration (AMD), glaucoma, diabetic retinopathy (DR), refractive error, and presbyopia. Early detection of eye disease can provide substantial benefits in prompting treatment to reduce progression and mitigate disability. Compared with other regions, South Asia has the most cases of visual impairment due to cataracts and uncorrected refractive error. The combination of poverty, poor living and working environments, and limited health care access have long endangered eye health in Bangladesh. Coastal Bangladesh is particularly impacted by eye disease due to economic deprivation and limited healthcare access. The coastal population mostly works in fishing and agriculture, have prolonged sunlight exposure, and inadequate occupational eye protection. This low-lying region, with 35 million people, is especially vulnerable to climate disasters and global warming. High rates of chronic disease, especially diabetes mellitus Type 2 and hypertension, coupled with limited screening and treatment, shape the area's health profile, with the increasing prevalence of eye diseases such as DR, glaucoma, and visual impairment. To address the issues of poor health, accessibility, and affordability of eye care, Artificial Intelligence (AI) applications, such as Artificial Intelligence (AI)-assisted fundus imaging, can be applied in eye screening. Medical AI applications have the potential to improve the quality and efficiency of healthcare, reduce healthcare costs, optimize treatment plans, and bolster the development of primary healthcare. They can identify presumptive DR, hypertensive retinopathy (HR), AMD, and glaucoma by analyzing the retina and optic disc of fundus images with moderate accuracy and high efficiency, thus helping address the lack of local eye care professionals. Data Yakka developed a human-AI collaboration that delivers affordable and transformative community-based eye screening to underserved communities in the coastal Bangladesh region of Char Fasson. The Amar Chokh Amar Alo (My Eyes, My Light) initiative creates and implements comprehensive eye screening that combines AI-assisted eye screening and grassroots partnerships with trusted non-health non-governmental organizations (NGOs). It has three objectives: 1) Enhancing accessibility and affordability of eye screening; 2) Supporting high quality and efficient treatment of those problems detected via screening, 3) Collecting fundus images to refine or train AI algorithms in the future. This project was designed to evaluate the feasibility, performance, equity, and cost of this model of eye screening and its implications for global eye disease. The implementation of participant recruitment, data collection, screening, and follow-up was separated into twelve steps. This standardized framework ensured the integration of screening with data collection and follow-up eye care services. Based on risk stratification by diabetes, hypertension, age 50+ years, and/or optometrist recommendation, fundus imaging was offered selectively to higher-risk patients.