In the thesis, the challenge of precisely developing a data-driven Linear Time Invariant MIMO Reticle Heating Induced Deformation Prediction (RHIDP) model for ASML's DUV systems is presented. The model is developed for two inputs, namely airflow temperature and dose for full field exposures. A reduced order data-driven based approach for developing a RHIDP model for various Use Cases (UC) is presented. This prediction model will be used for precisely estimating the global reticle deformation geometry as well as remove engendered non-Moving Average (non-MA) effects below the reticle surface. The identified linear regression model exhibits a very high degree of prediction accuracy for a broad working envelope, the prediction is accurate to within a range of 10^(-12) m and 10^(-10) m with 99% VAF values for all static inputs (UCs) and low frequency (<= 0.01 Hz) sine inputs. This model can thus be used for precise feed-forward correction of RH induced thermal deformation and non-MA effects