The background of digital financial inclusion and household financial forecasting is mainly concerned with the use of digital technologies to increase the penetration and accessibility of financial services, as well as forecasting households’ financial situation and needs through data analysis to support more accurate financial decisions and services. In today’s society, there are problems of low accuracy and efficiency of digital inclusion and family financial prediction, which greatly affect the accuracy of digital inclusion and family financial prediction. The K-means algorithm in machine learning is an effective digital inclusion and household financial forecasting technique. In this chapter, K-means algorithm is used to create a digital inclusion and family financial forecasting system, which greatly improves the accuracy and efficiency of digital inclusion and family financial forecasting. Finally, the experimental results show that the K-means algorithm is easy to operate and the accuracy rate is 97.3%.