In this study, the biosorption performance of banana floret was assessed as a new biosorbent for the removal of Cu(II) ions (a model heavy metal) from aqueous solutions. Batch experiments were conducted to assess the effects of agitation, particle size, pH, temperature and initial concentration. Kinetic and equilibrium data were modeled, and mass transfer studies were conducted to elucidate the mechanisms of biosorption. Kinetic data were best simulated using the diffusion-chemisorption model while equilibrium data were best represented by the Sips isotherm. The dominant transport mechanism was attributed to intraparticle diffusion while the dominant attachment mechanism was chemical sorption. A predictive model was successfully developed using an artificial neural network (ANN) and optimized using a genetic algorithm (GA). The accuracy of the ANN-GA prediction was validated by laboratory experiments, which revealed a residual error of 1.3% and thus underscores the applicability of the model. This new biosorbent exhibited a remarkable affinity for the heavy metal ion and compared well to other reported biosorbents in the literature.