This paper develops data-driven Type-2 Takagi-Sugeno (T-S) fuzzy modeling and control for bilateral teleoperation with dynamic uncertainties and time-varying delays. The Type-2 T-S fuzzy model identified based on input-output data samples describes the nonlinear teleoperation system by a weighted sum of a group of linear local models, which offers a platform to design robust control algorithms by means of mature linear theories. The fuzzy-model-based four-channel control laws are proposed to guarantee the motion synchronization and enhance the operator’s force perception for the environment when the time-varying delays and large dynamic uncertainties, especially the gravity of a heavy end effector of the slave, exist. Markov processes are applied to model the time delays. The stability of the closed-loop system is proved by using the Lyapunov-Krasovskii functions. All the conditions are expressed as linear matrix inequalities (LMIs). By using the Matlab LMI toolbox, the optimized control gains for each of the fuzzy rules are derived to achieve the optimal performance. Finally, experiments based on an experimental platform consisting of two haptic devices prove the superiority of the proposed strategy through comparison with previous work.