In today's world, the rapid advancement of 6G-enabled IoT networks has created high demands for energy efficiency, latency, and throughput. Existing research often publishes these performance metrics either in isolation or through bi-objective formulations, which cannot adequately represent the underlying trade-offs. This study introduces a tri-objective Pareto optimization framework for 6G IoT beamforming that simultaneously considers energy consumption, latency, and throughput. Using a real beamforming dataset, a Pareto analysis has been employed to uncover the empirical trade-off structure, and a surrogate regression model has been developed to approximate KPI interactions. Evolutionary optimization integration provides a smooth Pareto frontier and highlights clear operating zones under different IoT scenarios. The results reveal a strong nonlinear coupling among the three Key Performance Indicators and underscore the superiority of the proposed framework over previous single or bi-objective approaches. It will provide and help transparent and explainable decisions for beamforming configurations; this study is expected to establish a solid foundation for adaptive resource management in future 6G IoT systems.