Model predictive current control (MPCC) has emerged as a promising approach among various motor control strategies, due to its exceptional capabilities in control and speed regulation. This makes Internal Permanent Magnet Synchronous Motors (IPMSMs) highly utilized in motor drives and electric vehicles. Due to its ability to concurrently manage multiple variables and impose constraints conveniently. However, MPC relies heavily on mathematical models of the controlled system, which can lead to high computational demands and sensitivity to parameters. To address these challenges, an improved MPCC (IMPCC) algorithm for IPMSMs, incorporating an Adaline parameter identification method, has been developed based on the IPMSM model and traditional MPCC strategy. The computational complexity of the control strategy in conventional MPC is effectively mitigated by reducing the number of resultant voltage vectors per switching cycle. Secondly, integrating parameter identification into the predictive control framework enhances the adaptability and robustness of IPMSM control systems, thereby improving operational efficiency and reliability. Experimental results validate that this approach significantly reduces computational complexity while enhancing the disturbance rejection capabilities of the predictive control system, thereby minimizing the adverse effects of motor parameter disturbances on control performance.