This paper proposes a new Model Predictive Control scheme incorporating a Radial Basis Function Network Observer for the fuel injection problem. Two new contributions are presented here. First a Radial Basis Function Network is used as an observer for the air system. This allows for gradual adaptation of the observer, ensuring the control scheme is capable of maintaining good performance under changing engine conditions brought about by engine wear, variations between individual engines, and other similar factors. The other major contribution is the use of model predictive control algorithms to compensate for the fuel pooling effect on the intake manifold walls. Two model predictive control algorithms are presented which enforce input, and input and state constraints. In this way stability under the constraints is guaranteed. A comparison between the two constrained MPC algorithms is qualitatively presented, and conclusions are drawn about the necessity of constraints for the fuel injection problem. Simulation results are presented that demonstrate the effectiveness of the control scheme, and the proposed control approach is validated on a four-cylinder spark ignition engine.