With the development of urbanization during the past few decades, Municipal Solid Wastes (MSW) in urban areas have posed serious challenges to the local environment in many cities around the world, such as Macau. Residential and touristic activities are the two primary sources of solid waste in Macau; however, it is still unclear how and which indicators of these two could be used to predict solid waste generation. Taking advantage of the data published by government agencies in Macau from 2010 to 2021, our study explored the possibility of using publicly available data to predict the trend of monthly MSW generation in Macau. In particular, we adopted a machine learning strategy and compared six predictive models that implement both conventional and state-of-art machine learning methods. Our results indicate that the Generalized Additive Model outperformed the rest of the models in predicting MSW generation in Macau and is more suitable when data present strong seasonality and potential domain shift. Consistent with previous literature, both population-level and household-level factors contributed to MSW generation, including population density, income level, household energy consumption, and household size. In addition, our results support that tourism activities — especially tourists’ spending on non-traded goods and services—positively affected MSW generation.