<p>Underwater photography and communication devices are essential for ocean research, environmental monitoring, and military purposes. Nonetheless, these systems have distinct problems owing to the intricate underwater environment. Underwater photographs often exhibit low contrast, color distortion, noise, and inconsistent lighting due to light absorption and dispersion. Deep learning techniques, such as convolutional neural networks (CNNs), alongside traditional algorithms like Retinex and histogram equalization, have been employed for underwater image enhancement; however, they frequently encounter challenges such as elevated computational complexity, restricted adaptability to diverse aquatic conditions, and suboptimal color restoration. Poor data rates, significant latency, and detrimental environmental effects plague acoustic-based systems, despite their widespread use. This research provides a dual-objective strategy to tackle these difficulties. We provide an advanced particle swarm optimization (PSO)-based image improvement algorithm that dynamically categorizes picture channels and implements an adaptive gamma correction technique via real-time optimization. This method enhances contrast and visibility while preserving the authentic look of aquatic environments. Secondly, we present a light fidelity (Li-Fi)-based underwater communication system using laser light and on-off keying (OOK) modulation, executed with Arduino hardware. This technology facilitates the underwater transfer of text, audio, and picture data with high speed, low cost, and minimal latency. This work’s innovation is in the synthesis of real-time picture enhancement with optical wireless communication, providing a viable substitute for traditional acoustic systems. Collectively, these contributions enhance the dependability, velocity, and visual precision of underwater sensing and transmission.</p>