Autonomous robotic surgery offers enhanced effectiveness, precision, and reliability, regardless of the surgeons’ expertise. Prior neurosurgery robot studies involved surgeons manually assisting the robot in skull-milling tasks by holding the milling cutter shank, constraining the robot’s autonomy. A model-free adaptive nonlinear force control algorithm is designed to accomplish automatic cranial-milling tasks. Furthermore, a skull-milling breakthrough detection algorithm by monitoring the change of feed force is proposed to determine the completion of the milling task autonomously. A robotic system is developed for automatic cranium-milling and 72 in vitro skull-milling experiments indicate that when using the proposed control algorithm, the maximum root mean square error percentage of the vertical force is 0.99%, while the control error percentages of other mainstream methods are all above 5.5%. Moreover, the success rate of breakthrough detection is 98.61% and the robot autonomously performs the skull milling task with minimal human intervention during the whole experiment. The results demonstrate that the proposed method provides the potential to improve the intelligence of neurosurgery.