Accumulating evidence indicates that microorganisms respond to the ubiquitous plastic pollution by evolving plastic-degrading enzymes. However, the functional diversity of these enzymes and their distribution across the ocean, including the deep sea, remain poorly understood. By integrating bioinformatics and artificial intelligence-based structure prediction, we developed a structure- and function-informed algorithm to computationally distinguish functional polyethylene terephthalate-degrading enzymes (PETases) from variants lacking PETase activity (pseudo-PETase), either due to alternative substrate specificity or pseudogene origin. Through in vitro functional screening and in vivo microcosm experiments, we verified that this algorithm identified a high-confidence, searchable sequence motif for functional PETases capable of degrading PET. Metagenomic analysis of 415 ocean samples revealed 23 PETase variants, detected in nearly 80% of the samples. These PETases mainly occur between 1,000 and 2,000 m deep and at the surface in regions with high plastic pollution. Metatranscriptomic analysis further identified PETase variants that were actively transcribed by marine microorganisms. In contrast to their terrestrial counterparts-where PETases are taxonomically diverse-those in marine ecosystems were predominantly encoded and transcribed by members of the Pseudomonadales order. Our study underscores the widespread distribution of PETase-containing bacteria across carbon-limited marine ecosystems, identifying and distinguishing the PETase motif that underpins the functionality of these specialized cutinases.