The exponential surge in data transmission demands has surged pressures on existing radio frequency (rf) spectrum capacities, ushering in the necessity for advanced communication technologies. in this context, visible light communication (vlc) emerges as a transformative solution harnessing the underutilized frequency band spanning from 430 thz to 790 thz. the convergence of low-cost devices, particularly photodiodes (pds) and light-emitting diodes (leds), with the unique blend of "communication" and "illumination," propels vlc as an eco-friendly, cost-effective alternative to traditional rf communication methods. our proposal endeavors to revolutionize vlc technology by harnessing the potentials of optical orthogonal frequency division multiplexing (oofdm), multiple-input-multiple-output (mimo), and intelligent reflecting surfaces (irs) technologies. a pivotal facet of this initiative is the integration of bayesian learning (bl) strategies into various phases, elucidated through a comprehensive seven-phase timeline. the phased approach commences with phase 1, embarking on a comprehensive literature review and the implementation of an oofdm transceiver while initiating bl strategies for channel learning (cl) in oofdm vlc systems. subsequent phases delve into the development of novel vlc schemes within mimo-oofdm systems (phase 2), extending the framework to encompass massive mimo (mmimo) systems (phase 3), and studying bl variants in multi-user multi-cell (mu-mc) mmimo oofdm vlc systems (phase 4). further innovation unfolds in phase 5, exploring intelligent cl schemes in irs-aided vlc systems, and phase 6, investigating strategies for cl in irs-assisted mmimo-oofdm vlc systems. the culmination in phase 7 involves the development of bl techniques tailored for cl in irs-assisted mu-mc-mmimo-oofdm vlc systems. the crux of our innovation lies in the infusion of bl methodologies at every phase, leveraging observations and prior knowledge to determine posterior distributions of parameters. through iterative expectation-maximization (em) approaches, we seek to enhance sparse signal recovery, enabling robust data transmission, and error reduction within vlc systems. our research aims to significantly surpass existing compressive sensing-based approaches, ensuring heightened performance and reliability across diverse vlc setups. this synopsis encapsulates our pioneering approach, envisioning a paradigm shift in vlc technology through the integration of advanced technologies and the innovative application of bayesian learning strategies.