According to The Sustainable Development Goals (SDG) from the United Nations, the first goal is to "end poverty in all its forms everywhere". However, a common method to measure poverty is census data or large sample research, which collects data from a large sample size. The cost for conducting these researches is even higher in low-income areas due to the scarce infrastructure (Blumenstock, 2016; Jean et al., 2016; Perez et al., 2019, McBride&Nichols, 2015). As technology develops, scholars and researchers have begun to apply new techniques and massive machine-generated data sources to measure poverty. In this blog, I discuss three general trends in machine learning about poverty measurement and some concerns in the current application.