Detecting and quantifying biofouling is a challenging process inside a seawater reverse osmosis (SWRO) module due to its design complexity and operating obstacles. Herein, deep Convolutional Neural Network (CNN) models were developed to accurately calculate the cross-sectional biofilm thickness (vertical plane) through membrane surface images (horizontal plane). Models took membrane surface image as input; the classification model (CNN-Class) predicted fouling classification, while the regression model (CNN-Reg) predicted the average biofilm thickness on the membrane surface. CNN-Class model showed 90% accuracy, and CNN-Reg reached a moderate mean difference of ±24% in predicting the classification and biofilm thickness, respectively. Both models performed well and validated with 80% accuracy in classification and a mean difference of ±18% in biofilm thickness prediction from a new set of unseen live OCT images. The developed CNN models are a novel technology that has the potential to be implemented in desalination plants for early decision-making and biofouling mitigation.