Using synthetic disaster simulations, a machine learning-based multimodal forensic data fusion framework was developed for disaster victim identification (DVI). By integrating DNA, dental, and facial picture data from 10,000 synthetic files, the framework comprised 5,000 ante-mortem and 5,000 postmortem profiles. DNA data were represented using 200 synthetic SNP markers, while each dental phenotype was represented using features such as tooth count, treatment history, and bone loss. Also, facial data were represented as 64D ResNet-50 embeddings. The dataset was divided into training, validation, and test sets in a 70:15:15 ratio. Three unimodal models and two fusion strategies were evaluated in 10 independent runs. The proposed feature-level fusion model performed best, with 94.8 ± 0.4% accuracy, 0.94 ± 0.01 precision, 0.94 ± 0.01 recall, 0.94 ± 0.02 F1, and 0.97 ± 0.01 macro-averaged OvR ROC-AUC. The accuracy of decision-level fusion was 93.5% ± 0.5, which is better than the DNA-only (88.4% ± 0.6), dental-only (82.7% ± 0.7), and facial image-only (84.9% ± 0.5) classifiers. When one of the three models was missing, accuracy declined to more than 90.5%. Under severely degraded multimodal conditions, the accuracy was 88.7% ± 0.7. ANOVA revealed significant differences between unimodal and fusion models (F = 18.45, p = 0.0002). Attention weights were assigned as follows: 40% to DNA, 34% to dental, and 26% to facial data, which helps explain the interpretability. The results show good performance on synthetic DVI simulations but require testing on actual forensic data.