Currently, 3D Computational Fluid Dynamic (CFD) rotorcraft simulations are able to account for blade crossover interaction (BCI) events, which are impulsive loading events that have a large influence on the vibrations and acoustics of a vehicle. Unfortunately, lower fidelity models are unable to adequately predict the BCI events and 3D CFD simulations are computationally expensive. This paper proposes a surrogate model that is able to predict the BCI event with reasonable accuracy and high computational efficiency for a subset of operating conditions. A dataset was created using transient 2D CFD simulations of airfoils moving toward each other in close proximity. The Mach number, angle-of-attack of each airfoil, along with the vertical separation distance between airfoils was varied in each simulation. An additional dependent parametric input (airfoil horizontal separation distance) was recorded along with the transient loads on the airfoils. This dataset was used in a supervised manner to train univariate (UV) and multivariate (MV) implementations of the Gaussian Process Regression model. Given airfoil operating conditions, the models are able to predict the BCI events. Hyper-parameters such as kernels and trend functions were compared using 5-fold cross validation and the final MV and UV models were compared on a held out test set to demonstrate predictive performance on unseen data.