The study introduces a novel Radial Basis Function Neural Network-based Super-Twisting Sliding Mode Collective Blade Pitch Control (RBFNN-STSM-CBPC), designed specifically for semi-submersible platform-based Floating Offshore Wind Turbines (FOWTs) operating above rated speed (Region III). The proposed composite controller is developed using a refined nonlinear Control-Oriented Model, including lumped unmodeled dynamics and external disturbances. To our knowledge, this is the first time that a neural network STSM-CPBC approach is designed for this application. The RBFNN operates as an adaptive observer for the lumped disturbance, enhancing the robustness and performance of the standard STSM-CBPC for the same gains. Its adaptive law, formulated through the Lyapunov method, ensures stability and convergence by adjusting the adaptive weight. Simulation results demonstrate the superiority of the RBFNN-STSMCBPC over the standard STSM-CBPC method in regulating rotor speed and mitigating platform motion.