To address the issue of stress concentration in hydraulic slips during the gripping of drill pipes in deep well drilling, which often leads to drill pipe damage, a hybrid optimization method integrating Bayesian neural network (BNN) and artemisinin slime mold algorithm (ASMA) is proposed. Taking the hydraulic slip with flat-top tooth structure as the research object, a slip tooth-drill pipe contact model was first established, and the stress distribution of the slip tooth and the drill pipe was calculated through finite element analysis to extract the initial dataset. On this basis, orthogonal experiments were conducted to screen sensitive parameters, and the sample dataset was further expanded for surrogate model training. Subsequently, a BNN-based surrogate model was developed to fit the slip tooth profile parameters and mechanical responses (with a determination coefficient of R²>0.95), followed by multi-objective optimization utilizing the ASMA. The results demonstrated that the maximum equivalent stress of the slip tooth was reduced from 582.96 MPa to 303.53 MPa (a reduction of 47.9%), while the maximum equivalent stress of the drill pipe decreased from 360.03 MPa to 235.87 MPa (a decrease of 34.5%), significantly enhancing the performance of the slip tooth. The research results provide an efficient and reliable novel approach for the structural optimization of hydraulic slip tooth profiles.