For a class of strict-feedback nonlinear systems involving unknown time-varying parameters and external disturbances, an event-triggered adaptive prescribed-time control method is proposed. First, a prescribed-time adjustment function containing only a single reciprocal term is introduced to unify the multi-stage high-order scaling in conventional prescribed-time backstepping into a single first-order scaling, thereby simplifying the structure of the prescribed-time scaling term and reducing the complexity of the recursive controller design. Second, an adaptive law is developed to adaptively compensate for the effects of unknown time-varying parametric uncertainties, while a radial basis function neural network (RBFNN) is embedded into the backstepping framework to achieve real-time approximation and compensation of external disturbances. Moreover, an event-triggered mechanism is incorporated to operate synchronously with the controller, guaranteeing prescribed-time stability and substantially reducing the number of control updates compared with periodic sampling, thus saving communication and computational resources. Finally, numerical simulations demonstrate that the proposed control strategy achieves prescribed-time convergence and post-prescribed-time practical boundedness, while maintaining satisfactory robustness with significantly fewer control updates.