This paper introduces an ensemble deep learning method of Indian cattle breed classification with automatic classification, incorporating EfficientNetV2 and ResNet50V2 structures and transfer learning methodologies. Our method tackles essential difficulties in cattle breed determination such as variations in posture, interference from the background, visual similarity among breeds, and sparse training samples. The two neural structure utilizes complementary feature extraction, global average pooling, feature concatenation, and dropout regularization to promote robustness and accuracy. Comprehensive data augmentation methods such as rotation, flipping, and color changes further enhance model generalization. Experiments done on a dataset of 1,340 images of 10 Indian cattle breeds reached a validation accuracy rate of 80.81%, surpassing recent similar approaches by 2.61–5.91%. The model shows specific effectiveness in separating visually similar breeds that pose difficulty in traditional identification strategies. This platform is an easy and scalable method for cattle breed recognition, aiding conservation strategies and farming management with immediate applications in precision livestock farming, preservation of breeds, and monitoring systems.