Background Trachoma is a serious health problem in the world's poorest countries, such as Ethiopia. The WHO aims to eliminate trachoma by 2030 through implementation of annual mass drug administration and other strategies. With almost eight years left, is Ethiopia on track to reach the goal? This research article attempts to model the time to stop MDA in one of the nations with the highest prevalence oftrachoma in the world. Objectives Evaluation of predictive analytic models (Cox proportional-hazards model and the random survival forest) to model the time to stop trachoma mass drug administration in persistence districts of Ethiopia and identify factors that accelerate or decelerate time to stop trachoma mass drug administration Materials and Methods We propose survival and machine learning models to predict the time needed to stop trachoma MDA in Ethiopia using secondary data from the Tropical Data Platform and the Trachoma Elimination Monitor Form. The impact of average mass drug administration coverage, improved latrine coverage, access to improved water, delay in MDA intervention, TF prevalence in thefirst Trachoma Impact Survey (TIS1),and MDA omission were also assessed. Results The result shows that the probability of districts reaching the 5% threshold varies by region, and there are also discrepancies between districts that have delayed MDA and those that have not. We also note the significant effects of MDA coverage, latrine coverage, access to water supply, initial TIS score, and MDAomission on current TF score. Ourmodel also predicts that under the existing scenarios, there are districts that will not meet the 2030 goal of eliminating trachoma. Conclusions In order to stop trachoma MDA or eliminate the infection efficiently and effectively, it is crucial to identify the appropriate efficacy of drug, quality of MDA coverage, frequency, timing and number of rounds of MDA. Additionally, increase environmental and hygienic conditions may accelerate progress towards 2030 goals.