The quantitative characterization of thin-film dynamics is fundamental to understanding complex fluid-dynamic phenomena of a soap film. The chaotic nature, rapid evolution, and tendency of flow patterns on soap films to merge make their analysis based on image processing via traditional methods exceedingly difficult. This thesis presents a hybrid computational pipeline that integrates supervised and unsupervised deep learning with classic computer vision approaches to address this challenge. For semantic segmentation, a U-Net architecture was trained on a dataset of manually annotated masks, enhanced by a sophisticated localized data augmentation strategy and a weighted loss function. In parallel, a Convolutional Autoencoder was employed for unsupervised clustering via K-Means to identify different flow regimes.