Visible Light Communication (VLC) systems employing multiple Light-Emitting Diode (LED) transmitters face significant challenges from aggregate nonlinear distortions that degrade signal quality and limit practical deployment. This work presents experimental validation of Random Vector Functional Link (RVFL) networks for post-distortion compensation in Multiple-Input Single-Output (MISO) VLC systems. RVFL’s closed-form training and memoryless architecture offer distinct computational advantages over iterative methods. Comprehensive experimental validation across variable drive amplitude (300-900 mV) and bias current (50-350 mA) in a 2×1 dual-luminaire testbed demonstrates that RVFL achieves EVM of 7.81% for 16-QAM and 3.97% for 64-QAM at 200 cm distance, enabling reliable transmission where uncompensated signals exceed thresholds. Comparative analysis reveals superior computational efficiency: RVFL inference requires 45.5 ms versus 502.5 ms for Artificial Neural Networks (ANN) and 338.5 ms for Volterra, achieving 11× faster inference than ANN. These results position RVFL as an attractive candidate for practical VLC receivers where computational efficiency is critical.