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Abstract Amphiphilic copolymers (AP) represent a class of novel antibiofouling materials whose chemistry and composition can be tuned to optimize their performance. However, the enormous chemistry‐composition design space associated with AP makes their performance optimization laborious; it is not experimentally feasible to assess and validate all possible AP compositions even with the use of rapid screening methodologies. To address this constraint, a robust model development paradigm is reported, yielding a versatile machine learning approach that accurately predicts biofilm formation by Pseudomonas aeruginosa on a library of AP. The model excels in extracting underlying patterns in a “pooled” dataset from various experimental sources, thereby expanding the design space accessible to the model to a much larger selection of AP chemistries and compositions. The model is used to screen virtual libraries of AP for identification of best‐performing candidates for experimental validation. Initiated chemical vapor deposition is used for the precision synthesis of the model‐selected AP chemistries and compositions for validation at solid–liquid interface (often used in conventional antifouling studies) as well as the air–liquid–solid triple interface. Despite the vastly different growth conditions, the model successfully identifies the best‐performing AP for biofilm inhibition at the triple interface.