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Underwater Image Enhancement (UIE) encounters substantial challenges due to the intricate nature of physical degradation processes that diminish visibility in underwater images. Existing methods leverage learning-based models to delineate pixel mappings for either paired or unpaired images to ameliorate the quality of degraded visuals. Nonetheless, the paucity of paired image datasets and the erratic nature of unsupervised learning methods considerably hinder advancements in UIE. This study presents an innovative contrastive learning framework specifically designed for UIE, aimed at effectively addressing the aforementioned challenges. Our strategy reconceptualizes image enhancement as a multi-task joint learning problem, thus fortifying the enhancement process. We pinpoint three pivotal aspects for UIE: contrastive feature learning, semantic information coherence, and cross-domain feature transfer. These elements are imperative for augmenting contrast, preserving texture integrity, and ensuring color accuracy. The contrastive learning paradigm empowers the enhancement module to discern between unpaired positive (high-quality) and negative (degraded) underwater images, facilitating semantic learning in refining the enhancement network systematically. By leveraging the semantic feature domain extracted from unpaired high-quality images, our method demonstrates superior performance, validated by several quality metrics, outperforming recent advancements in unsupervised UIE techniques.