The application of statistical modeling approach with the predictive capability of sinter density and grain size is perceived as a central theme in the development of next generation ceramics. Such computationally intensive method can be equally significant, if the predicted process conditions can be adapted experimentally to develop complex shaped ceramics with variable sizes. In the first ever attempt to address such issues for the ceramics, we have, in the present work, considered a range of factors and levels from relevant process variables (sintering temperature, sintering time) and material variables (sinter-aid addition and reinforcement content) as input parameters to formulate data-driven, high throughput analytical assays by response surface method (RSM). Using ZrO2 toughened Al2O3 (ZTA) as a model system, the adopted RSM approach has been used to quantitatively predict independent and interactive role of process and material variables on sinter density and grain size.