The number of daily sUAS operations in uncontrolled low altitude airspace is expected to reach into the millions in a few years. Therefore, UAS density prediction has become an emerging and challenging problem. In this paper, a machine learning-based UAS instantaneous density prediction model is presented. The model takes two types of data as input: 1) the historical density generated from the historical data, and 2) the future sUAS mission information. The architecture of our model contains four components: Historical Density Formulation module, UAS Mission Translation module, Mission Feature Extraction module, and Density Map Projection module. The training and testing data are generated by a python based simulator which is inspired by the multi-agent air traffic resource usage simulator (MATRUS) framework. The quality of prediction is measured by the correlation score and the Area Under the Receiver Operating Characteristics (AUROC) between the predicted value and simulated value. The experiment results demonstrate outstanding performance of the machine learning-based UAS density predictor. Compared to the baseline models, for simplified traffic scenario where no-fly zones and safe distance among sUASs are not considered, our model improves the prediction accuracy by up to 15.2% and its correlation score reaches 0.947. In a more realistic scenario, where the no-fly zone avoidance and the safe distance among sUASs are maintained using A∗ routing algorithm, our model can still achieve 0.822 correlation score. Meanwhile, the AUROC can reach 0.951 for the hot spot prediction. Finally, we extend our UAS instantaneous density prediction model to a continuous prediction framework. By applying the continuous prediction framework, the UAS density prediction time horizon can be significantly increased from 60 simulation cycles to 360 simulation cycles (1 hour), with highest 0.892 correlation score on average. This feature grants us a chance to apply our density prediction model in real-word scenarios.