<p>The performance of species distribution models is influenced by model algorithms, and the form of occurrence/non-occurrence data. Therefore, selecting an appropriate approach based on the objective and type/size of the modeling data is essential for reducing model uncertainty. In this study, we used a range of algorithm-based single models to predict the habitat suitability of <i>Corythucha marmorata</i> (chrysanthemum lace bug) worldwide and developed ensemble models using different methods, including mean, median, committee averaging, and weighted mean, so that they could be further applied to a specific region (South Korea). In addition, we tested the pseudo-absence data generation methods (random, surface range envelope, and Disk) using a combination of ensemble modeling methods in terms of model performance. Among the three methods, the TSS of the committee averaging algorithm and the weighted mean algorithm with the surface range envelope method were the highest at 0.980 and 0.977, respectively. These models were used to predict the potential distribution of <i>C. marmorata</i> in South Korea, showing a high probability of occurrence throughout the country, except on the southernmost island. Through this study, we expected to provide insights into the methodological use of species distribution modeling by incorporating various algorithm-based models, ensemble methods, and data preprocessing techniques.</p>