Enset, Enset ventricosum, is a crop that contributes for approximately 20% of the total population in Ethiopia depends upon Enset for food security. The yield prediction is a major issue that remains to be solved based on available data. It is a crucial function in planning for food security of the population of a district or even of the whole country. Enset yields estimation models accounting the inter clonal, age group, and harvesting time differences to predict the different yield products (kocho in two forms, bulla, amicho, and fiber) of an Enset plant non-destructively are still lacking. As Enset has five yields/products, the already developed Enset yield estimation model is having a limited use in that only works for 'kocho' yield estimation. The prediction models for 'bulla', 'amicho' and fiber yield estimations are not yet developed. In this research we used Artificial Neural Network model for Enset yield prediction to predict the amounts of different Enset yields (kocho in two forms, bulla, amicho, and fiber) of an Enset plant. The objective of this research to design and develop an Enset Yield Prediction Model Using Machine Learning Algorithms: In case Of Wolayta, Ethiopia. The Enset data was presented in the form of numerical and it was collected from Areka Agricultural Research center for the 7 years (2013 to 2019). An exhaustive study is performed on the given dataset and algorithms. The research approach has five phases, data gathering, data pre-processing, the prediction model is implemented to predict yields, the model is trained and finally, the model is evaluated. We have built MLP-ANN, RF model and the Ensemble MLP-ANN. We have evaluated the performance of the models. We also compared the results based on the errors generated. The results of comparing the three models are: - For the model RF we got with R2, MSE, and RMSE 0.81, 0.176, and 0.419 respectively, and for the model MLP-ANN we got with R2, MSE, and RMSE 0.857, 0.14, and 0.374 respectively. And also for newly proposed model which is EMLP-ANN, we have evaluated for R2, MSE, and RMSE 0.92, 0.077, and 0.277 respectively. viii The study result show that using stacking ensemble method for MLP-ANN enables to come up with better prediction. This also can be improved by using the combined approach of the more advanced machine learning algorithms