###### Abstract  
Recent advances in species distribution models (SDMs) associated with artificial intelligence (AI) and increased volumes of available data for model variables have allowed reliable evaluation of the potential distribution of any species. A reliable SDM requires suitable occurrence records and variables with optimal model structures. In this study, we developed three different machine learning-based SDMs [MaxEnt, random forest (RF), and multi-layer perceptron (MLP)] to predict the global potential distribution of two invasive ants under current and future climates. These SDMs showed that the potential distribution of _Solemopsis invicita_ would be expanded by climatic change, whereas it would not significantly change for _Anoploplis graclipes_. The models were compared using model performance metrics, and the optimal model structure and spatial projection were selected. The MaxEnt exhibited high performance, while the MLP model exhibited low performance, with the largest variation by climate change. Random forest showed the smallest potential distribution area, but it was robust considering the number of occurrence records and changes in model variables. All the models showed reliable performance, but the difference in performance and projection size suggested that optimal model selection based on data availability, model variables, study objectives, or an ensemble approach was necessary to develop a comprehensive SDM to minimize modeling uncertainty. We expect that this study will help with the use of AI-based SDMs for the evaluation and risk assessment of invasive ant species. Anoploplis graclipes; artificial intelligence; climate change; _Solemopsis invicita_; species distribution modeling 2  
perceptron (MLP) is the most recent approach that attempts to classify the potential or predicted presence and absence of a species. The application of MLP is relatively limited because of the challenges in determining the model structure and insufficient data to perform deep learning [4]. Nevertheless, advances in computing large data volumes and global concerns about invasive pests are accelerating the use of machine learning-based SDMs [5]. Therefore, there is an ongoing need for research to improve the performance of these models and optimize the selection of suitable models based on the target species, model variables, and data availability [6]. Ants are an intelligent species with high environmental adaptability and dispersion speed based on sociality through swarming; some invasive species have caused severe ecosystem damage [7]. _Solenopsis invicta_ (Hymenoptera: Formicidae) and _Anoplolepis gracilipes_ (Hymenoptera: Formicidae) are the most aggressive invasive ant species, and a global conservation campaign is underway to track and manage their invasions [8; 9]. _Solenopsis invicta_, originating from South America and widely distributed throughout the US, has migrated to Asia and Australia [9; 10; 11]. _Anoplolepis gracilipes_ is a tropical species that mostly inhabits southern Asia and a few areas in Central America and Oceania [8; 10]. Their habitats are likely to expand; therefore, they have been selected as the target species for SDM because of the severe damage induced by them in the invaded regions [12; 13; 14; 15; 16; 17]. Recently, enhancing the performance of an SDM has been issued, emphasizing high-quality data for model variables, advanced spatial processing, and a model using multiple machine learning algorithms. While there are a few sources to secure datasets for model variables, and spatial analysis tools for processing them, studies comparing machine learning-based models at the same time are limited. The high invasive risk of these ant species suggests a need to compare the model performance of the different types of machine learning-based SDMs to select the most appropriate model to identify the potential colonization areas of the ant species. The SDM characteristics can be analyzed in terms of data availability, model structures, and performance.