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Mosquito-transmitted diseases, particularly dengue, chikungunya, and Zika pose an increasing public health challenge in Southeast Asia where climate change and rapid land-use change are altering transmission dynamics and associated risks. As primary vectors of these diseases, Aedes aegypti predominates in densely urbanized and peri‑urban environments, exploiting artificial containers for oviposition, while Ae. albopictus, historically found in rural and suburban areas, tends to expand its ecological range worldwide and occupies a broader range of landscapes, including forested and peri‑urban areas. Temperature, rainfall, and humidity influence their survival and reproduction, shaping where each species can thrive under different climatic conditions. These contrasting preferences reflect specific climatic tolerances and landscape associations observed along gradients throughout the region.Climatic factors such as temperature, precipitation, and relative humidity are identified as determinants for distribution and abundance of Ae. aegypti and Ae. albopictus, although their effects vary seasonally and geographically. Remote sensing and GIS-based studies have further highlighted the role of vegetation indices and urban land cover in shaping vector suitability. Geographic gaps exist in Southeast Asia, where most species distribution modeling studies are limited to local or national scales. This is largely due to the lack of standardized and comprehensive mosquito occurrence data, the concentration of studies in more easily accessible areas, and the challenges of harmonizing data across countries. As a result, regional models remain limited, impeding comprehensive assessment of the environmental drivers of Aedes at broader scales. Many existing models lack justification for variable selection and rarely address multicollinearity among predictors, limiting interpretability and robustness. To fill these gaps, standardized methodologies must be put in place that rigorously test correlations between environmental determinants in order to improve the predictive capacity of distribution models relevant to public health planning and vector control.Here, we develop a species distribution modeling (SDM) framework that combines statistical and machine learning approaches to quantify the environmental drivers of Ae. aegypti and Ae. albopictus across mainland Southeast Asia. By combining entomological data from Global Biodiversity Information Facility (GBIF) and local datasets provided by project partners, and integrating satellite-derived land-cover classifications, landscape metrics, and high-resolution bioclimatic variables, we evaluate the importance of climatic and landscape predictors while considering collinearity and scale effects. Model performance is evaluated using spatial cross-validation to ensure transferability across countries. Our results provide spatially explicit maps of Aedes mosquitoes habitat suitability and identify key environmental determinants driving current distributions across Southeast Asian countries. We discuss how these determinants may evolve under ongoing and future climate change and on the potential consequences for Aedes suitability patterns and implication for climate-sensitive disease risk. This perspective highlights the relevance of our findings for surveillance prioritization, targeted vector control strategies, and the development of data-driven early warning systems supporting climate-resilient public health planning in Southeast Asia.