The study focuses on addressing the significant challenges posed by retinal diseases to global healthcare systems, particularly in Kenya. Rapid and accurate detection is crucial for effective treatment. This research explores the use of RF machine learning techniques to enhance retinal disease detection at Kenya's KHN hospital. The study aims to develop and train a RF model using retinal imaging data, comparing its accuracy with conventional diagnostic methods. The goal is to assess the feasibility and cost-effectiveness of implementing this model in Kenya's healthcare system. The research utilizes a large dataset of retinal imaging from KHN patients. The RF model is trained using advanced machine learning techniques to classify various retinal diseases accurately. A comparative analysis is conducted, demonstrating the model's higher accuracy compared to traditional diagnostic methods. The study's results, based on a landmark trial in 2021, confirm the promising findings. The RF model exhibits high accuracy in detecting and classifying retinal diseases, surpassing the efficiency of conventional methods. This improved accuracy facilitates early diagnosis, enabling swift interventions and potentially enhancing patient outcomes. Additionally, a feasibility study indicates that implementing the RF model in Kenya's healthcare system could yield economic benefits, optimize resource allocation, and enhance national hospital efficiency. However, the study acknowledges limitations. It emphasizes the need for caution when generalizing results beyond KHN and Kenya's specific population. Factors such as data availability, quality, ethical considerations, model interpretability, and implementation challenges are identified as potential limitations requiring careful consideration and further research. In a nutshell, the study pioneers the use of RF machine learning for retinal disease detection, demonstrating superior accuracy compared to conventional methods. While showing promise for Kenya's healthcare system, the research emphasizes the importance of addressing various challenges and limitations in broader applications.