To address the problem of deviation in calculating the azimuth angle of the drilling tool caused by the measurement error of the magnetic in-place magnetometer, a magnetic inertial beluga whale optimization (MIBWO) based error parameter identification method for the magnetic in-place magnetometer is proposed. Firstly, the output error model of the magnetometer is formulated, the objective function is constructed, and the constraint conditions are established by utilizing the connection between the magnetometer data and the acceleration and gyroscope data. Then, in view of the insufficiency of the beluga whale optimization (BWO) in optimizing the magnetic error parameters in the while-drilling environment, a dimensional adaptive small-hole imaging opposite learning strategy is proposed, three adaptive learning factors are designed to adjust the step size and direction of the individual in searching the magnetic error parameters, and the golden sine search strategy is introduced to improve BWO to obtain MIBWO. Finally, evaluation is carried out through simulation and actual drilling experiments. The results show that the error parameters of the magnetometer identified by the MIBWO algorithm have a significant effect on the error compensation of the magnetometer, and the average value of the absolute error of the calculated azimuth angle is reduced from 5. 1° to 0. 9°. This method can effectively improve the measurement accuracy of the magnetometer.