In this study, an induction motor (IM) drive based on speed-sensorless predictive torque control (PTC) is designed to perform the high-performance control of the IMs by utilizing the least mean square (LMS) algorithm for the adaptation mechanism of the model reference adaptive system (MRAS). Here, the MRAS with LMS adaptation is based on the stator currents (i_sα and i_sβ) of the IM. Moreover, the rotor fluxes (φ_rα and φ_rβ) are obtained by the current model, which requires the rotor mechanical speed (ω_m) along with i_sα and i_sβ. In contrast to the other MRAS based studies using proportional-integral (PI) in the adaptation mechanisms to estimate state or parameter, it is possible determine the states and/or parameters as weight coefficients in the MRAS with LMS adaptation which is calculated and updated in each iteration. Here, ω_m value is estimated and updated in each iteration as weight coefficient. Furthermore, one of the most preferred model predictive control (MPC) strategies, PTC, is used to in this study obtain robust control of the IM. The simulation results clearly visualize both the estimation performance of stator current based MRAS and the effectiveness of the proposed PTC based IM drive