Electrocardiogram (ECG)-based gender identification, which utilizes the electrical activity of the heart, has emerged as a promising approach in biometric and healthcare applications. This study introduces DeepFusion-CNN, a context-aware fusion framework that integrates VGG-19, DenseNet-121, and ResNet-152 using a validation-driven adaptive weighting strategy to improve gender classification performance. Unlike conventional ensemble approaches that use a static averaging strategy, the proposed approach adaptively adjusts each sub-model's contribution based on its validation performance, enabling improved feature representation and classification robustness. This adaptive fusion mechanism allows better-performing models to contribute more significantly, leading to improved overall prediction accuracy compared to individual models and static fusion strategies. The ECG signals are preprocessed using band-pass filtering, followed by R-peak identification using the Pan–Tompkins algorithm. The processed signals are then segmented and converted into 225×225×3 two-dimensional images, making them suitable for transfer learning with pre-trained convolutional models. To maintain a fair evaluation, the data is partitioned on a subject basis before any augmentation, and augmentation is restricted to the training portion only. The framework is evaluated on the PTB and CYBHi datasets, achieving accuracies of 99.08% and 99.13%, respectively. Ablation test results indicate that the feature quality and classification performance are improved after preprocessing and the context-aware fusion strategy. The proposed framework shows strong potential for ECG-based gender classification and could serve as a useful foundation for future advancements in biometric systems and healthcare applications.