Accurate modeling of the internal flow and spray char-acteristics in fuel injectors is a critical aspect of direct injection engine design. However, such high-fidelity computational fluid dynamics (CFD) models are often computationally expensive due to the requirement of resolving fine temporal and spatial scales. This paper addresses the computational bottleneck issue by proposing a machine learningbased emulator framework, which learns efficient surrogate models for spatiotemporal flow distributions relevant for static coupling injection maps, namely total void fraction, velocity, and mass, within a design space of interest. Different design points involving variations of needle lift, fuel viscosity, and level of non-condensable gas in the fuel were explored in this study. An interpretable Bayesian learning strategy was employed to understand the effect of the design parameters on the void fraction fields at the exit of the injector orifice. The results show a strong influence of the amount of non-condensable gas on the level of cavitation as well as the overall shape of the gas-phase structures at the orifice exit. The emulator framework involves the construction of deep autoencoders for efficient dimensionality reduction of the flowfields. Deep artificial neural networks were then employed for prediction of the flowfields for unknown operating conditions. The emulated flowfields were then tested by evaluating spray and combustion predictions from one-way coupling spray simulations. The analysis of the spray predictions from CFD-generated and emulator-predicted injections maps revealed that the emulation framework is capable of reproducing spray predictions with similar level of accuracy, yet at a fraction of the computational cost. The maximum achievable speed-up using the emulator framework is up to 2 million times over the traditional CFD approach for generating static coupling injection maps. The emulation framework provides an efficient pathway for integrating detailed injector simulations into spray and engine simulations.