Peste des Petits Ruminants (PPR) is one of the common diseases that has been affecting animals especially goats and sheep. Despite of having many solutions to counter this disease still there is a challenge in detection of the disease using the symptoms. This study aimed to develop a machine learning model that can effectively classify the disease using the observable symptoms. Using the datasets collected in Tanzania this work has developed and evaluated models using four machine learning algorithms which are Logistic Regression, K-nearest neighbor, Support Vector Classifier, and Decision Tree. Further, the study investigated the influence of imbalanced dataset on the performance of the developed models using subsampling technique. According to the results the best score was 79% on sampled data and 81% on unsampled data. This score was achieved by both Logistic regression and Support Vector Machine algorithms.