Determining the uniformity and consistency of droplet size in the dispersed phase is key to emulsion stability. Conventional methods, which include manual microscopic evaluation and laser diffraction, have presented many challenges to achieve an accurate evaluation of droplet dispersion in pharmaceutical emulsions. Artificial intelligence techniques have demonstrated potential in overcoming the subjectivity and time-consumption associated with the manual approaches in industry. A new automated machine learning approach is presented in this study to predict in-process emulsion quality from micrographs. Droplet characteristics are extracted from emulsion micrographs using a histogram-based image segmentation technique. Machine learning classification models are developed, with a selected set of droplet characteristics as predictors, via Random Forest, Multinomial Logistic Regression and Vanilla Neural Network to classify the micrographs into four categories. The hyper-parameters of the models are tuned using 10-fold cross validation. A pixel-based Convolutional Neural Network model is also investigated. Random Forest presented the best accuracy of 99.78% compared to the deep learning models, which presented a bias towards the high frequency classes. The automated machine learning approach demonstrated promising potential for inline emulsion quality evaluation.