The reactivation of faults is governed by the variation of effective stresses in the fault's plane. These effective stresses are dependent on the total stresses (solely related to the regional geological stresses and lithology) and pore pressure (strongly affected by rock properties, fluid content, and saturation conditions). Injecting fluids into a reservoir formation may change the distribution of pore pressures, which influences the effective stresses and may cause the reactivation of existing faults, which has a wide range of consequences. This study investigated the reactivation of preexisting faults due to fluid injection into hydrocarbon reservoirs at different pressures and temperatures. A 3D model containing a continuous normal fault that divides the domain into two compartments was used. A user-defined constitutive model based on continuum damage mechanics implemented as a Fortran subroutine to predict the behavior of fractured and faulted reservoirs was used. A parametric analysis was performed to examine the influence of geometric parameters, such as the fault dip angle, reservoir characteristics, and fluid injection parameters. A machine learning approach based on artificial neural networks (ANNs) is incorporated to predict the enhanced oil recovery using fluid injection. The results predicted by the ANN were further confirmed by numerical modeling.