Identification of novel cancer driver mutations is crucial for targeted cancer therapy, yet a difficult task especially for low frequency drivers. To identify cancer driver mutations, we developed a machine learning (ML) model to predict cancer hotspots. Here, we applied the ML program to 32 non-receptor tyrosine kinases (NRTKs) and identified 36 potential cancer driver mutations, with high probability mutations in 10 genes, including ABL1, ABL2, JAK1, JAK3, and ACK1. ACK1 is a member of the poorly understood ACK family of NRTKs that also includes TNK1. Although ACK1 is an established oncogene and high-interest therapeutic target, the exact mechanism of ACK1 regulation is largely unknown and there is still no ACK1 inhibitor in clinical use. The ACK kinase family has a unique domain arrangement with most notably, a predicted ubiquitin association (UBA) domain at its C-terminus. While the presence of a functional UBA domain on a kinase is unique to the ACK family, but the role of the UBA domain on ACK1 is unknown. Interestingly, the ML program identified the ACK1 Mig6 homology region (MHR) and UBA domains truncating mutation p633fs* as a cancer driver mutation. Our data suggest that the ACK1 UBA domain helps activate full-length ACK1 through induced proximity. It also acts as a mechanism of negative feedback by tethering ACK1 to ubiquitinated cargo that is ultimately degraded. Indeed, our preliminary data suggest that truncation of the ACK1 UBA stabilizes ACK1 protein levels, which results in spontaneous ACK1 oligomerization and activation. Furthermore, our data suggests removal of the MHR domain hyper activates ACK1. Thus, our data provide a model to explain how human mutations in ACK1 convert the kinase into an oncogenic driver. In conclusion, our data reveal a mechanism of ACK1 activation and potential strategies to target the kinase in cancer.