With the increasing prevalence of omnidirectional mobile robots in industrial applications, such as collaborative transportation and cargo classification, the demand for computational power in these robots has grown significantly. Model Predictive Control (MPC) is widely used for trajectory tracking due to its exceptional ability to handle constraints; however, it is computationally intensive. Therefore, our core approach proposes a hybrid event-triggering mechanism to minimize the reliance on MPC. When the tracking error remains within a specified threshold, the system continues using the existing optimal control sequence without resolving the MPC optimization problem, thereby reducing computational complexity. However, less frequent use of MPC can lead to decreased tracking accuracy. To address this issue, we incorporate a novel sliding mode observer to compensate for errors and mitigate the effects of unknown disturbances. To validate the performance of the proposed controller, we conducted simulations comparing the trajectory tracking performance of traditional MPC, event-triggered MPC, and observer-based MPC under disturbance conditions. The results demonstrate that the proposed algorithm maintains tracking accuracy while significantly reducing computational load.