Temperature control is one of the most important processes during aluminum (Al) alloy engine cylinder head product casting. An improper temperature control may result in no uniformity and microstructure defects in casting parts and give rise to high defect ratio. In this paper, a mathematical model with high nonlinearity, strong coupling, and less uncertainty is developed for the solidification process in Al alloy casting. The interfacial heat transfer coefficient is combined with the mold structure comprehensively to build the temperature-structure model, and the characteristics of the uncertainty conversion are also used in order to achieve optimal temperature control during the solidification process. The cloud model integrated with Proportion-Integral-Differential (PID) temperature control system enables evaluation of the uncertainty conversion quantitatively. By inputting the temperature error and the temperature error rate, the PID inference is output through the cloud inference engine to achieve the optimal temperature curve. The superiority of the control algorithm was verified on a customized experimental platform with the temperature control system. Compared with manual operation and traditional PID control, the result shows that the error of the cloud model control is lower than the manual operation and traditional PID control. The experimental results also suggest that the performance of our cloud model is better than that of the manual operation model and the traditional PID control model regarding to stability and controllability.