Some congenital heart malformations develop because of cardiac abnormalities at mid-gestation that produces abnormal biomechanical environment and causes abnormal cardiac development. Fetal aortic stenosis (AS) with evolving Hypoplastic Left Heart Syndrome (eHLHS) is an example. The stenosis decreases myocardial strain and stroke volume, increase in left ventricular (LV) blood pressure, and causes mitral regurgitation (MR). The subsequent poor LV growth results in HLHS at birth. In these cases, catheter-based fetal aortic valvuloplasty (FAV) intervention is shown to be able to resolve the obstruction, restore normative biomechanics, and prevent HLHS at birth in substantial number of cases. However, the biomechanics of the disease or the intervention is not well-understood, and our ability to predict outcomes is poor, preventing accurate patient selection and preventing better outcomes. We developed a workflow to perform Finite Element (FE) Modelling of fetal hearts in a patient-specific way, based on 4D echocardiography images, and used this to study the biomechanics of disease features and of the intervention. We find that AS can significantly elevate LV pressures, lengthen systolic duration ratio, and decimate stroke volume, depending on severity, and that MR reduced these abnormalities. Our model of diseased LV could not produce valve velocities as high as clinically measured values unless wall thickening occurs, and this corroborated with clinical observations of LV wall thickening in diseased hearts. Our model further suggested that weakened contractions occurred, and that contrary to current believes, fibroelastosis is unlikely to impede cardiac function. Modelling of FAV intervention is performed through the reduction of aortic flow resistance, and is found to be able to normalize biomechanical abnormalities. Our results showed that Image-based FE modeling can be a good way to enhance our understanding of fetal heart malformation physiology, and to evaluate fetal heart interventions, and may be good tools to predict intervention outcomes.