Over the past few decades, the Sahel region has become a hotbed of Islamist violence. Mali can be considered the original epicenter of this unfortunate development and a frontrunner in the effects of this increased instability. Jama’at Nasr Al Islam Wal Muslimin (JNIM) is a terrorist group which is responsible for much of the terror activity in the region. This chapter summarizes the main results of this book. We first assembled a 12 year dataset consisting both of different types of attacks by JNIM, as well as the environment in which JNIM has operated during this time. The chapter reports on a comprehensive machine-learning based analysis of these 144 months of data, identifying the key conditions and variables that are linked to different types of attacks by JNIM. The chapter reports on these key variables. In addition, the chapter reports on one full year of forecasts about attacks by JNIM over time periods varying from 1 month into the future to 6 months into the future. These machine learning forecasts are thoroughly analyzed. The chapter concludes with a discussion of the implications of using such machine learning models and forecasts for military decision making.