The use of organic solvents in various industries poses significant environmental risks. Deep eutectic solvents (DESs) have emerged as a promising alternative due to their environmentally friendly properties. However, finding a suitable DES for a specific application remains a challenge. Empirical selection has been the most prominent approach despite being resource-intensive and time-consuming. With recent advances in artificial intelligence (AI), the scientific community is presented with an opportunity to employ powerful machine learning methods to facilitate and speed up this process. In this study, we aimed to explore this opportunity in application to the design of DESs. We propose an approach to predict the physicochemical properties of DESs focusing on melting temperature, density, and viscosity. For that, we assembled a comprehensive database of two- and three-component DESs, characterized by a range of descriptors related to the three properties. We trained machine learning models on these data and evaluated their performance using cross-validation accuracy metrics. We found that gradient-boosted trees demonstrated superior performance compared to other models. With CatBoost, we achieved cross-validation R² values of 0.76, 0.89, and 0.64, predicting melting temperature, density, and viscosity, respectively. Furthermore, we developed a web-resource, DESignSolvents, to provide users worldwide with the database of DES properties and the corresponding prediction models. We hope this resource will serve as a valuable tool for researchers and industry professionals to efficiently select and design DESs for various applications, promoting the spread of green chemistry.