Multizone pressure chemical mechanical planarization (CMP), which is regarded as irreplaceable in semiconductor manufacturing, is highly dependent on a process control strategy. However, predictive models with strict rules for advanced CMP multizone pressure process control have rarely been examined, even with the development of machine learning applications in semiconductor fabrication. Establishing a precise data-driven model that takes the fabrication conditions into consideration is urgent for controlling the CMP process. A novel structure for the CMP material removal rate model consisting of two parts is proposed and validated according to split pressure experiments and feature importance learning by this article. The first part was devoted to the prediction of the polishing time and time dependent problem with a deep neural network. The other part focused on the mutual effect of the pressure distribution on the multizone material removal rate using deep neural network with fully connected layers. To improve the prediction model of the first part, this study devises an advanced predictive model that combines a bidirectional gated recurrent unit network with a multi-head self-attention module and a one-dimensional neural network. This deep network extracts time-varying factors from industrial dataset and establish a nonlinear mapping function from CMP process features to material removal rate with no pressure variables. Both part of this structure is tested and evaluated for the feasibility of the predictive model using dataset from industrial fabrication.