Congenital heart defects (CHD) are the most common birth defect which causes an estimated problem in 40,000 newborns every year in the United States and up to 9.1 out of every 1000 global live births. Although the development of surgical interventions has seen five-year survival rates of more than 90% in dedicated facilities, CHD is the number-one cause of infant mortality related to a congenital anomaly, with 204,223 people succumbing to it in 2021, across all countries. The existing methods of diagnosis are based on single-modality imaging that has its own limitations such as up to 25% of inter-observer variability in echocardiographic examinations and 10–15% false-negative complications in complex lesions. A new multimodal artificial intelligence formulation that uses echocardiography, cardiac magnetic resonance imaging, and computed tomography and synthesizes the capabilities to improve the detection and risk stratification of CHD is introduced in this study. Our deep learning pipeline boasted better diagnostic value of 0.94 area under the curve, sensitivity of 91%, and specificity of 89%, performing much better in comparison with single-modality strategies. The risk stratification algorithm had a concordance value of 0.87 in predicting adverse cardiac outcomes, which could lead to specific early interventions that would result in reducing the huge economic cost of more than 2.1 million dollars over the lifetime cost per patient.