Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3801 2018 2018 Department of Information and Communication, Al-khwarizmi College of Engineering, University of Baghdad, Iraq Ayah Ayah Department of Information and Communication, Al-khwarizmi College of Engineering, University of Baghdad, Iraq Lwaa F. Abdulameer Department of Information and Communication, Al-khwarizmi College of Engineering, University of Baghdad, Iraq Heba M. Fadhil Visible light communication (VLC) integrated with nonorthogonal multiple access (NOMA) is a promising technique to meet the increasing demand for high capacity, energy-efficient communication in forthcoming 6G networks. This work thoroughly evaluates VLC-NOMA systems and emphasizes the incorporation of machine learning (ML) approaches to improve spectrum efficiency, the bit error rate, and resource allocation. A technique based on Preferred Reporting Items for Systematic Reviews and Meta-analyses produced 244 records, among which 45 were selected for comprehensive study. The review identified obstacles, including scalability, computational complexity, and insufficient experimental validation. A comparative examination elucidated the strengths and limits of machine learning methodologies, including machine learning, deep neural networks, and federated learning, in addressing these difficulties. The study identified key research gaps, proposed future directions, and emphasized the need for hybrid optimization techniques, lightweight machine learning models, and real-world implementations. The findings contribute to the development of robust, scalable VLC-NOMA systems for 6G applications. 2025 2025 34 54 10.54216/FPA.200104 https://www.americaspg.com/articleinfo/3/show/3801