The rapid spread of infectious diseases poses a significant global health challenge, requiring timely and accurate detection for effective intervention. Traditional disease detection services, such as the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO), play a crucial role in monitoring and responding to outbreaks. However, these services are largely inaccessible to people around the world due to their high costs and resource-intensive processes because they often rely on expensive sources of data. Fortunately, satellite images are a great alternative source of data as modern satellites can provide detailed images which clearly display a region’s financial status and pollution levels: two key metrics in potential disease outbreaks. Therefore, this study aimed on developing a more affordable algorithm (SatNet) that utilizes publicly available satellite imagery to perform disease hotspot detection. The algorithm works by retrieving zoomed-in satellite images of the city inputted by the user and feeding these images into a novel, hybrid recursive convolutional neural network. This model, designed to classify regions within the images as low income, high-income, or industrial areas, was trained and tested on a custom data set consisting of 7,448 images and was able to achieve a 94.872 training accuracy and 84.183 testing accuracy. The output of this model is then used to create a detailed heatmap for the city which clearly indicates the specific regions in most danger of disease outbreaks. Overall, the affordability and accessibility of SatNet will allow governments/organizations around the world to provide their people with the healthcare they need and significantly reduce the spread of diseases in an increasingly interconnected world.