Objective To investigating the safety and accuracy of artificial intelligence (AI) assisted automatic planning of pedicle screws parallel to sagittal plane for C1. Methods The subjects who completed cervical CT scan in Zigong Fourth People’s Hospital btween January 2020 and December 2023 were selected. The subjects who completed cervical CT scan were randomly divided into two groups using a random number table method. Among them, 80% were used as the training model (training group), and 20% were used as the validation model (validation group). The original cervical CT data of the training group were imported into ITK-SNAP software to mark the feature points. Four feature points were selected. In order to obtain the weighted function model of the four feature points, training group were trained with the spatial key point location algorithm. pedicle trajectory based on the four key points obtained. Finally, the algorithm was compiled to form a visual interface, and imported into the verification group of annular vertebral CT data to calculate the pedicle screw trajectory. Results A total of 500 patients were included. Among them, there were 400 cases in the training group and 100 cases in the validation group. The average positioning error of spatial key points is (0.47±0.16) mm. The average distance between the planned pedicle screw center line and the internal edge of the pedicle was (2.86±0.12) mm. Pedicle screw placement parallel to the sagittal plane and 3D display can be safely performed for the C1 pedicle that is large enough to accommodate a 3.5 mm diameter screw without cortical breakthrough. Conclusions For pedicle screw planning parallel to the sagittal plane in C1, training based on the spatial positioning algorithm of anterior and posterior tubercles and bilateral tangential points can obtain a safe and accurate pedicle screw trajectory. It provides theoretical basis for orthopedic robot automatic screw placement. For vertebral bodies with narrow or deformed pedicles, further expansion of the training data is needed to expand the adaptive range and improve the accuracy of the algorithm.