The addition of telescopic joints will enhance the manipulator’s motion dexterity but increase the motion complexity, thereby potentially reducing the efficiency of motion planning. To address the issue, a manipulability priority principle is used to reduce the computational dimension, aiming to enhance computational efficiency. Firstly, an improved Monte Carlo algorithm combined with Weighted Ensemble Learning is proposed to rapidly obtain a well-distributed and information-complete reachable workspace under small-sample conditions. Then, all dexterous points are computed by the manipulability formula and the least squares algorithm is applied to obtain the boundary of the reachable workspace. Based on the boundary, a spatial quadratic surface-based segmentation algorithm is proposed to obtain the dexterous workspace corresponding to all dexterous points. Subsequently, the Telescopic Motion Computation (TMC) method is designed to transform the kinematics problem of a 9-DOF manipulator into a 7-DOF problem by precomputing the telescopic joint lengths, effectively reducing the computational time for inverse kinematics. Finally, simulations are performed to verify the effectiveness, dexterity, and efficiency of the TMC method. The results indicate that applying the TMC method to PInv, DLS, and PSO algorithms enhances the efficiency by 3.43, 3.53, and 3.64 times while maintaining the motion dexterity.